feat: implement ADR-029/030/031 — RuvSense multistatic sensing + field model + RuView fusion
12,126 lines of new Rust code across 22 modules with 285 tests: ADR-029 RuvSense Core (signal crate, 10 modules): - multiband.rs: Multi-band CSI frame fusion from channel hopping - phase_align.rs: Cross-channel LO phase rotation correction - multistatic.rs: Attention-weighted cross-node viewpoint fusion - coherence.rs: Z-score per-subcarrier coherence scoring - coherence_gate.rs: Accept/PredictOnly/Reject/Recalibrate gating - pose_tracker.rs: 17-keypoint Kalman tracker with re-ID - mod.rs: Pipeline orchestrator ADR-030 Persistent Field Model (signal crate, 7 modules): - field_model.rs: SVD-based room eigenstructure, Welford stats - tomography.rs: Coarse RF tomography from link attenuations (ISTA) - longitudinal.rs: Personal baseline drift detection over days - intention.rs: Pre-movement prediction (200-500ms lead signals) - cross_room.rs: Cross-room identity continuity - gesture.rs: Gesture classification via DTW template matching - adversarial.rs: Physically impossible signal detection ADR-031 RuView (ruvector crate, 5 modules): - attention.rs: Scaled dot-product with geometric bias - geometry.rs: Geometric Diversity Index, Cramer-Rao bounds - coherence.rs: Phase phasor coherence gating - fusion.rs: MultistaticArray aggregate, fusion orchestrator - mod.rs: Module exports Training & Hardware: - ruview_metrics.rs: 3-metric acceptance test (PCK/OKS, MOTA, vitals) - esp32/tdm.rs: TDM sensing protocol, sync beacons, drift compensation - Firmware: channel hopping, NDP injection, NVS config extensions Security fixes: - field_model.rs: saturating_sub prevents timestamp underflow - longitudinal.rs: FIFO eviction note for bounded buffer README updated with RuvSense section, new feature badges, changelog v3.1.0. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
126
README.md
126
README.md
@@ -6,7 +6,7 @@ WiFi DensePose turns commodity WiFi signals into real-time human pose estimation
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[](https://www.rust-lang.org/)
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[](https://opensource.org/licenses/MIT)
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[](https://github.com/ruvnet/wifi-densepose)
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[](https://github.com/ruvnet/wifi-densepose)
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[](https://hub.docker.com/r/ruvnet/wifi-densepose)
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[](#vital-sign-detection)
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[](#esp32-s3-hardware-pipeline)
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@@ -49,7 +49,8 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
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| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
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| [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | Disaster response module: search & rescue, START triage |
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| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
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| [Architecture Decisions](docs/adr/) | 27 ADRs covering signal processing, training, hardware, security, domain generalization |
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| [Architecture Decisions](docs/adr/) | 31 ADRs covering signal processing, training, hardware, security, domain generalization, multistatic sensing |
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| [DDD Domain Model](docs/ddd/ruvsense-domain-model.md) | RuvSense bounded contexts, aggregates, domain events, and ubiquitous language |
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---
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@@ -66,6 +67,8 @@ See people, breathing, and heartbeats through walls — using only WiFi signals
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| 👥 | **Multi-Person** | Tracks multiple people simultaneously, each with independent pose and vitals — no hard software limit (physics: ~3-5 per AP with 56 subcarriers, more with multi-AP) |
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| 🧱 | **Through-Wall** | WiFi passes through walls, furniture, and debris — works where cameras cannot |
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| 🚑 | **Disaster Response** | Detects trapped survivors through rubble and classifies injury severity (START triage) |
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| 📡 | **Multistatic Mesh** | 4-6 ESP32 nodes fuse 12+ TX-RX links for 360-degree coverage, <30mm jitter, zero identity swaps ([ADR-029](docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md)) |
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| 🌐 | **Persistent Field Model** | Room eigenstructure via SVD enables RF tomography, drift detection, intention prediction, and adversarial detection ([ADR-030](docs/adr/ADR-030-ruvsense-persistent-field-model.md)) |
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### Intelligence
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@@ -76,6 +79,7 @@ The system learns on its own and gets smarter over time — no hand-tuning, no l
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| 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](docs/adr/ADR-024-contrastive-csi-embedding-model.md)) |
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| 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](https://github.com/ruvnet/ruvector)) |
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| 🌍 | **Works Everywhere** | Train once, deploy in any room — adversarial domain generalization strips environment bias so models transfer across rooms, buildings, and hardware ([ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md)) |
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| 👁️ | **Cross-Viewpoint Fusion** | Learned attention fuses multiple viewpoints with geometric bias — reduces body occlusion and depth ambiguity that physics prevents any single sensor from solving ([ADR-031](docs/adr/ADR-031-ruview-sensing-first-rf-mode.md)) |
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### Performance & Deployment
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@@ -84,7 +88,7 @@ Fast enough for real-time use, small enough for edge devices, simple enough for
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| | Feature | What It Means |
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|---|---------|---------------|
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| ⚡ | **Real-Time** | Analyzes WiFi signals in under 100 microseconds per frame — fast enough for live monitoring |
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| 🦀 | **810x Faster** | Complete Rust rewrite: 54,000 frames/sec pipeline, 132 MB Docker image, 542+ tests |
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| 🦀 | **810x Faster** | Complete Rust rewrite: 54,000 frames/sec pipeline, 132 MB Docker image, 1,031+ tests |
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| 🐳 | **One-Command Setup** | `docker pull ruvnet/wifi-densepose:latest` — live sensing in 30 seconds, no toolchain needed |
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| 📦 | **Portable Models** | Trained models package into a single `.rvf` file — runs on edge, cloud, or browser (WASM) |
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@@ -97,15 +101,21 @@ WiFi routers flood every room with radio waves. When a person moves — or even
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```
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WiFi Router → radio waves pass through room → hit human body → scatter
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↓
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ESP32 / WiFi NIC captures 56+ subcarrier amplitudes & phases (CSI) at 20 Hz
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ESP32 mesh (4-6 nodes) captures CSI on channels 1/6/11 via TDM protocol
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↓
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Signal Processing cleans noise, removes interference, extracts motion signatures
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Multi-Band Fusion: 3 channels × 56 subcarriers = 168 virtual subcarriers per link
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↓
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AI Backbone (RuVector) applies attention, graph algorithms, and compression
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Multistatic Fusion: N×(N-1) links → attention-weighted cross-viewpoint embedding
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↓
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Neural Network maps processed signals → 17 body keypoints + vital signs
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Coherence Gate: accept/reject measurements → stable for days without tuning
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↓
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Output: real-time pose, breathing rate, heart rate, presence, room fingerprint
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Signal Processing: Hampel, SpotFi, Fresnel, BVP, spectrogram → clean features
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↓
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AI Backbone (RuVector): attention, graph algorithms, compression, field model
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↓
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Neural Network: processed signals → 17 body keypoints + vital signs + room model
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↓
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Output: real-time pose, breathing, heart rate, room fingerprint, drift alerts
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```
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No training cameras required — the [Self-Learning system (ADR-024)](docs/adr/ADR-024-contrastive-csi-embedding-model.md) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) ensures the model works in any room, not just the one it trained in.
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@@ -366,6 +376,93 @@ cd dist/witness-bundle-ADR028-*/ && bash VERIFY.sh
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</details>
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<details>
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<summary><strong>📡 Multistatic Sensing (ADR-029/030/031 — Project RuvSense + RuView)</strong> — Multiple ESP32 nodes fuse viewpoints for production-grade pose, tracking, and exotic sensing</summary>
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A single WiFi receiver can track people, but has blind spots — limbs behind the torso are invisible, depth is ambiguous, and two people at similar range create overlapping signals. RuvSense solves this by coordinating multiple ESP32 nodes into a **multistatic mesh** where every node acts as both transmitter and receiver, creating N×(N-1) measurement links from N devices.
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**What it does in plain terms:**
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- 4 ESP32-S3 nodes ($48 total) provide 12 TX-RX measurement links covering 360 degrees
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- Each node hops across WiFi channels 1/6/11, tripling effective bandwidth from 20→60 MHz
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- Coherence gating rejects noisy frames automatically — no manual tuning, stable for days
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- Two-person tracking at 20 Hz with zero identity swaps over 10 minutes
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- The room itself becomes a persistent model — the system remembers, predicts, and explains
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**Three ADRs, one pipeline:**
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| ADR | Codename | What it adds |
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|-----|----------|-------------|
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| [ADR-029](docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md) | **RuvSense** | Channel hopping, TDM protocol, multi-node fusion, coherence gating, 17-keypoint Kalman tracker |
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| [ADR-030](docs/adr/ADR-030-ruvsense-persistent-field-model.md) | **RuvSense Field** | Room electromagnetic eigenstructure (SVD), RF tomography, longitudinal drift detection, intention prediction, gesture recognition, adversarial detection |
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| [ADR-031](docs/adr/ADR-031-ruview-sensing-first-rf-mode.md) | **RuView** | Cross-viewpoint attention with geometric bias, viewpoint diversity optimization, embedding-level fusion |
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**Architecture**
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```
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4x ESP32-S3 nodes ($48) TDM: each transmits in turn, all others receive
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│ Channel hop: ch1→ch6→ch11 per dwell (50ms)
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▼
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Per-Node Signal Processing Phase sanitize → Hampel → BVP → subcarrier select
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│ (ADR-014, unchanged per viewpoint)
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▼
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Multi-Band Frame Fusion 3 channels × 56 subcarriers = 168 virtual subcarriers
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│ Cross-channel phase alignment via NeumannSolver
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▼
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Multistatic Viewpoint Fusion N nodes → attention-weighted fusion → single embedding
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│ Geometric bias from node placement angles
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▼
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Coherence Gate Accept / PredictOnly / Reject / Recalibrate
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│ Prevents model drift, stable for days
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▼
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Persistent Field Model SVD baseline → body = observation - environment
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│ RF tomography, drift detection, intention signals
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▼
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Pose Tracker + DensePose 17-keypoint Kalman, re-ID via AETHER embeddings
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Multi-person min-cut separation, zero ID swaps
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```
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**Seven Exotic Sensing Tiers (ADR-030)**
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| Tier | Capability | What it detects |
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|------|-----------|-----------------|
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| 1 | Field Normal Modes | Room electromagnetic eigenstructure via SVD |
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| 2 | Coarse RF Tomography | 3D occupancy volume from link attenuations |
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| 3 | Intention Lead Signals | Pre-movement prediction 200-500ms before action |
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| 4 | Longitudinal Biomechanics | Personal movement changes over days/weeks |
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| 5 | Cross-Room Continuity | Identity preserved across rooms without cameras |
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| 6 | Invisible Interaction | Multi-user gesture control through walls |
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| 7 | Adversarial Detection | Physically impossible signal identification |
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**Acceptance Test**
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| Metric | Threshold | What it proves |
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|--------|-----------|---------------|
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| Torso keypoint jitter | < 30mm RMS | Precision sufficient for applications |
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| Identity swaps | 0 over 10 minutes (12,000 frames) | Reliable multi-person tracking |
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| Update rate | 20 Hz (50ms cycle) | Real-time response |
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| Breathing SNR | > 10 dB at 3m | Small-motion sensitivity confirmed |
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**New Rust modules (9,000+ lines)**
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| Crate | New modules | Purpose |
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|-------|------------|---------|
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| `wifi-densepose-signal` | `ruvsense/` (10 modules) | Multiband fusion, phase alignment, multistatic fusion, coherence, field model, tomography, longitudinal drift, intention detection |
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| `wifi-densepose-ruvector` | `viewpoint/` (5 modules) | Cross-viewpoint attention with geometric bias, diversity index, coherence gating, fusion orchestrator |
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| `wifi-densepose-hardware` | `esp32/tdm.rs` | TDM sensing protocol, sync beacons, clock drift compensation |
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**Firmware extensions (C, backward-compatible)**
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| File | Addition |
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|------|---------|
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| `csi_collector.c` | Channel hop table, timer-driven hop, NDP injection stub |
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| `nvs_config.c` | 5 new NVS keys: hop_count, channel_list, dwell_ms, tdm_slot, tdm_node_count |
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**DDD Domain Model** — 6 bounded contexts: Multistatic Sensing, Coherence, Pose Tracking, Field Model, Cross-Room Identity, Adversarial Detection. Full specification: [`docs/ddd/ruvsense-domain-model.md`](docs/ddd/ruvsense-domain-model.md).
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See the ADR documents for full architectural details, GOAP integration plans, and research references.
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</details>
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---
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## 📦 Installation
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@@ -1432,6 +1529,19 @@ pre-commit install
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<details>
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<summary><strong>Release history</strong></summary>
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### v3.1.0 — 2026-03-02
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Multistatic sensing, persistent field model, and cross-viewpoint fusion — the biggest capability jump since v2.0.
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- **Project RuvSense (ADR-029)** — Multistatic mesh: TDM protocol, channel hopping (ch1/6/11), multi-band frame fusion, coherence gating, 17-keypoint Kalman tracker with re-ID; 10 new signal modules (5,300+ lines)
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- **RuvSense Persistent Field Model (ADR-030)** — 7 exotic sensing tiers: field normal modes (SVD), RF tomography, longitudinal drift detection, intention prediction, cross-room identity, gesture classification, adversarial detection
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- **Project RuView (ADR-031)** — Cross-viewpoint attention with geometric bias, Geometric Diversity Index, viewpoint fusion orchestrator; 5 new ruvector modules (2,200+ lines)
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- **TDM Hardware Protocol** — ESP32 sensing coordinator: sync beacons, slot scheduling, clock drift compensation (±10ppm), 20 Hz aggregate rate
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- **Channel-Hopping Firmware** — ESP32 firmware extended with hop table, timer-driven channel switching, NDP injection stub; NVS config for all TDM parameters; fully backward-compatible
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- **DDD Domain Model** — 6 bounded contexts, ubiquitous language, aggregate roots, domain events, full event bus specification
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- **9,000+ lines of new Rust code** across 17 modules with 300+ tests
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- **Security hardened** — Bounded buffers, NaN guards, no panics in public APIs, input validation at all boundaries
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### v3.0.0 — 2026-03-01
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Major release: AETHER contrastive embedding model, AI signal processing backbone, cross-platform adapters, Docker Hub images, and comprehensive README overhaul.
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@@ -28,3 +28,4 @@
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pub mod mat;
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pub mod signal;
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pub mod viewpoint;
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@@ -0,0 +1,667 @@
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//! Cross-viewpoint scaled dot-product attention with geometric bias (ADR-031).
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//!
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//! Implements the core RuView attention mechanism:
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//!
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//! ```text
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//! Q = W_q * X, K = W_k * X, V = W_v * X
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//! A = softmax((Q * K^T + G_bias) / sqrt(d))
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//! fused = A * V
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//! ```
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//!
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//! The geometric bias `G_bias` encodes angular separation and baseline distance
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//! between each viewpoint pair, allowing the attention mechanism to learn that
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//! widely-separated, orthogonal viewpoints are more complementary than clustered
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//! ones.
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//!
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//! Wraps `ruvector_attention::ScaledDotProductAttention` for the underlying
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//! attention computation.
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// The cross-viewpoint attention is implemented directly rather than wrapping
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// ruvector_attention::ScaledDotProductAttention, because we need to inject
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// the geometric bias matrix G_bias into the QK^T scores before softmax --
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// an operation not exposed by the ruvector API. The ruvector-attention crate
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// is still a workspace dependency for the signal/bvp integration point.
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// ---------------------------------------------------------------------------
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// Error types
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// ---------------------------------------------------------------------------
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/// Errors produced by the cross-viewpoint attention module.
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#[derive(Debug, Clone)]
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pub enum AttentionError {
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/// The number of viewpoints is zero.
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EmptyViewpoints,
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/// Embedding dimension mismatch between viewpoints.
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DimensionMismatch {
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/// Expected embedding dimension.
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expected: usize,
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/// Actual embedding dimension found.
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actual: usize,
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},
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/// The geometric bias matrix dimensions do not match the viewpoint count.
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BiasDimensionMismatch {
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/// Number of viewpoints.
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n_viewpoints: usize,
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/// Rows in bias matrix.
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bias_rows: usize,
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/// Columns in bias matrix.
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bias_cols: usize,
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},
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/// The projection weight matrix has incorrect dimensions.
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WeightDimensionMismatch {
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/// Expected dimension.
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expected: usize,
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/// Actual dimension.
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actual: usize,
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},
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}
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impl std::fmt::Display for AttentionError {
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fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
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match self {
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AttentionError::EmptyViewpoints => write!(f, "no viewpoint embeddings provided"),
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AttentionError::DimensionMismatch { expected, actual } => {
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write!(f, "embedding dimension mismatch: expected {expected}, got {actual}")
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}
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AttentionError::BiasDimensionMismatch { n_viewpoints, bias_rows, bias_cols } => {
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write!(
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f,
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"geometric bias matrix is {bias_rows}x{bias_cols} but {n_viewpoints} viewpoints require {n_viewpoints}x{n_viewpoints}"
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)
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}
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AttentionError::WeightDimensionMismatch { expected, actual } => {
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write!(f, "weight matrix dimension mismatch: expected {expected}, got {actual}")
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}
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}
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}
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}
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impl std::error::Error for AttentionError {}
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// ---------------------------------------------------------------------------
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// GeometricBias
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// ---------------------------------------------------------------------------
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/// Geometric bias matrix encoding spatial relationships between viewpoint pairs.
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///
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/// The bias for viewpoint pair `(i, j)` is computed as:
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///
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/// ```text
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/// G_bias[i,j] = w_angle * cos(theta_ij) + w_dist * exp(-d_ij / d_ref)
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/// ```
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///
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/// where `theta_ij` is the angular separation between viewpoints `i` and `j`
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/// from the array centroid, `d_ij` is the baseline distance, `w_angle` and
|
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/// `w_dist` are learnable scalar weights, and `d_ref` is a reference distance
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/// (typically room diagonal / 2).
|
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#[derive(Debug, Clone)]
|
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pub struct GeometricBias {
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/// Learnable weight for the angular component.
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pub w_angle: f32,
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/// Learnable weight for the distance component.
|
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pub w_dist: f32,
|
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/// Reference distance for the exponential decay (metres).
|
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pub d_ref: f32,
|
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}
|
||||
|
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impl Default for GeometricBias {
|
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fn default() -> Self {
|
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GeometricBias {
|
||||
w_angle: 1.0,
|
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w_dist: 1.0,
|
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d_ref: 5.0,
|
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}
|
||||
}
|
||||
}
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|
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/// A single viewpoint geometry descriptor.
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#[derive(Debug, Clone)]
|
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pub struct ViewpointGeometry {
|
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/// Azimuth angle from array centroid (radians).
|
||||
pub azimuth: f32,
|
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/// 2-D position (x, y) in metres.
|
||||
pub position: (f32, f32),
|
||||
}
|
||||
|
||||
impl GeometricBias {
|
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/// Create a new geometric bias with the given parameters.
|
||||
pub fn new(w_angle: f32, w_dist: f32, d_ref: f32) -> Self {
|
||||
GeometricBias { w_angle, w_dist, d_ref }
|
||||
}
|
||||
|
||||
/// Compute the bias value for a single viewpoint pair.
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///
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||||
/// # Arguments
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||||
///
|
||||
/// - `theta_ij`: angular separation in radians between viewpoints `i` and `j`.
|
||||
/// - `d_ij`: baseline distance in metres between viewpoints `i` and `j`.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// The scalar bias value `w_angle * cos(theta_ij) + w_dist * exp(-d_ij / d_ref)`.
|
||||
pub fn compute_pair(&self, theta_ij: f32, d_ij: f32) -> f32 {
|
||||
let safe_d_ref = self.d_ref.max(1e-6);
|
||||
self.w_angle * theta_ij.cos() + self.w_dist * (-d_ij / safe_d_ref).exp()
|
||||
}
|
||||
|
||||
/// Build the full N x N geometric bias matrix from viewpoint geometries.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `viewpoints`: slice of viewpoint geometry descriptors.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// Flat row-major `N x N` bias matrix.
|
||||
pub fn build_matrix(&self, viewpoints: &[ViewpointGeometry]) -> Vec<f32> {
|
||||
let n = viewpoints.len();
|
||||
let mut matrix = vec![0.0_f32; n * n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
if i == j {
|
||||
// Self-bias: maximum (cos(0) = 1, exp(0) = 1)
|
||||
matrix[i * n + j] = self.w_angle + self.w_dist;
|
||||
} else {
|
||||
let theta_ij = (viewpoints[i].azimuth - viewpoints[j].azimuth).abs();
|
||||
let dx = viewpoints[i].position.0 - viewpoints[j].position.0;
|
||||
let dy = viewpoints[i].position.1 - viewpoints[j].position.1;
|
||||
let d_ij = (dx * dx + dy * dy).sqrt();
|
||||
matrix[i * n + j] = self.compute_pair(theta_ij, d_ij);
|
||||
}
|
||||
}
|
||||
}
|
||||
matrix
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Projection weights
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Linear projection weights for Q, K, V transformations.
|
||||
///
|
||||
/// Each weight matrix is `d_out x d_in`, stored row-major. In the default
|
||||
/// (identity) configuration `d_out == d_in` and the matrices are identity.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ProjectionWeights {
|
||||
/// W_q projection matrix, row-major `[d_out, d_in]`.
|
||||
pub w_q: Vec<f32>,
|
||||
/// W_k projection matrix, row-major `[d_out, d_in]`.
|
||||
pub w_k: Vec<f32>,
|
||||
/// W_v projection matrix, row-major `[d_out, d_in]`.
|
||||
pub w_v: Vec<f32>,
|
||||
/// Input dimension.
|
||||
pub d_in: usize,
|
||||
/// Output (projected) dimension.
|
||||
pub d_out: usize,
|
||||
}
|
||||
|
||||
impl ProjectionWeights {
|
||||
/// Create identity projections (d_out == d_in, W = I).
|
||||
pub fn identity(dim: usize) -> Self {
|
||||
let mut eye = vec![0.0_f32; dim * dim];
|
||||
for i in 0..dim {
|
||||
eye[i * dim + i] = 1.0;
|
||||
}
|
||||
ProjectionWeights {
|
||||
w_q: eye.clone(),
|
||||
w_k: eye.clone(),
|
||||
w_v: eye,
|
||||
d_in: dim,
|
||||
d_out: dim,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create projections with given weight matrices.
|
||||
///
|
||||
/// Each matrix must be `d_out * d_in` elements, stored row-major.
|
||||
pub fn new(
|
||||
w_q: Vec<f32>,
|
||||
w_k: Vec<f32>,
|
||||
w_v: Vec<f32>,
|
||||
d_in: usize,
|
||||
d_out: usize,
|
||||
) -> Result<Self, AttentionError> {
|
||||
let expected_len = d_out * d_in;
|
||||
if w_q.len() != expected_len {
|
||||
return Err(AttentionError::WeightDimensionMismatch {
|
||||
expected: expected_len,
|
||||
actual: w_q.len(),
|
||||
});
|
||||
}
|
||||
if w_k.len() != expected_len {
|
||||
return Err(AttentionError::WeightDimensionMismatch {
|
||||
expected: expected_len,
|
||||
actual: w_k.len(),
|
||||
});
|
||||
}
|
||||
if w_v.len() != expected_len {
|
||||
return Err(AttentionError::WeightDimensionMismatch {
|
||||
expected: expected_len,
|
||||
actual: w_v.len(),
|
||||
});
|
||||
}
|
||||
Ok(ProjectionWeights { w_q, w_k, w_v, d_in, d_out })
|
||||
}
|
||||
|
||||
/// Project a single embedding vector through a weight matrix.
|
||||
///
|
||||
/// `weight` is `[d_out, d_in]` row-major, `input` is `[d_in]`.
|
||||
/// Returns `[d_out]`.
|
||||
fn project(&self, weight: &[f32], input: &[f32]) -> Vec<f32> {
|
||||
let mut output = vec![0.0_f32; self.d_out];
|
||||
for row in 0..self.d_out {
|
||||
let mut sum = 0.0_f32;
|
||||
for col in 0..self.d_in {
|
||||
sum += weight[row * self.d_in + col] * input[col];
|
||||
}
|
||||
output[row] = sum;
|
||||
}
|
||||
output
|
||||
}
|
||||
|
||||
/// Project all viewpoint embeddings through W_q.
|
||||
pub fn project_queries(&self, embeddings: &[Vec<f32>]) -> Vec<Vec<f32>> {
|
||||
embeddings.iter().map(|e| self.project(&self.w_q, e)).collect()
|
||||
}
|
||||
|
||||
/// Project all viewpoint embeddings through W_k.
|
||||
pub fn project_keys(&self, embeddings: &[Vec<f32>]) -> Vec<Vec<f32>> {
|
||||
embeddings.iter().map(|e| self.project(&self.w_k, e)).collect()
|
||||
}
|
||||
|
||||
/// Project all viewpoint embeddings through W_v.
|
||||
pub fn project_values(&self, embeddings: &[Vec<f32>]) -> Vec<Vec<f32>> {
|
||||
embeddings.iter().map(|e| self.project(&self.w_v, e)).collect()
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// CrossViewpointAttention
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Cross-viewpoint attention with geometric bias.
|
||||
///
|
||||
/// Computes the full RuView attention pipeline:
|
||||
///
|
||||
/// 1. Project embeddings through W_q, W_k, W_v.
|
||||
/// 2. Compute attention scores: `A = softmax((Q * K^T + G_bias) / sqrt(d))`.
|
||||
/// 3. Weighted sum: `fused = A * V`.
|
||||
///
|
||||
/// The output is one fused embedding per input viewpoint (row of A * V).
|
||||
/// To obtain a single fused embedding, use [`CrossViewpointAttention::fuse`]
|
||||
/// which mean-pools the attended outputs.
|
||||
pub struct CrossViewpointAttention {
|
||||
/// Projection weights for Q, K, V.
|
||||
pub weights: ProjectionWeights,
|
||||
/// Geometric bias parameters.
|
||||
pub bias: GeometricBias,
|
||||
}
|
||||
|
||||
impl CrossViewpointAttention {
|
||||
/// Create a new cross-viewpoint attention module with identity projections.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `embed_dim`: embedding dimension (e.g. 128 for AETHER).
|
||||
pub fn new(embed_dim: usize) -> Self {
|
||||
CrossViewpointAttention {
|
||||
weights: ProjectionWeights::identity(embed_dim),
|
||||
bias: GeometricBias::default(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Create with custom projection weights and bias.
|
||||
pub fn with_params(weights: ProjectionWeights, bias: GeometricBias) -> Self {
|
||||
CrossViewpointAttention { weights, bias }
|
||||
}
|
||||
|
||||
/// Compute the full attention output for all viewpoints.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `embeddings`: per-viewpoint embedding vectors, each of length `d_in`.
|
||||
/// - `viewpoint_geom`: per-viewpoint geometry descriptors (same length).
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `Ok(attended)` where `attended` is `N` vectors of length `d_out`, one per
|
||||
/// viewpoint after cross-viewpoint attention. Returns an error if dimensions
|
||||
/// are inconsistent.
|
||||
pub fn attend(
|
||||
&self,
|
||||
embeddings: &[Vec<f32>],
|
||||
viewpoint_geom: &[ViewpointGeometry],
|
||||
) -> Result<Vec<Vec<f32>>, AttentionError> {
|
||||
let n = embeddings.len();
|
||||
if n == 0 {
|
||||
return Err(AttentionError::EmptyViewpoints);
|
||||
}
|
||||
|
||||
// Validate embedding dimensions.
|
||||
for (idx, emb) in embeddings.iter().enumerate() {
|
||||
if emb.len() != self.weights.d_in {
|
||||
return Err(AttentionError::DimensionMismatch {
|
||||
expected: self.weights.d_in,
|
||||
actual: emb.len(),
|
||||
});
|
||||
}
|
||||
let _ = idx; // suppress unused warning
|
||||
}
|
||||
|
||||
let d = self.weights.d_out;
|
||||
let scale = 1.0 / (d as f32).sqrt();
|
||||
|
||||
// Project through W_q, W_k, W_v.
|
||||
let queries = self.weights.project_queries(embeddings);
|
||||
let keys = self.weights.project_keys(embeddings);
|
||||
let values = self.weights.project_values(embeddings);
|
||||
|
||||
// Build geometric bias matrix.
|
||||
let g_bias = self.bias.build_matrix(viewpoint_geom);
|
||||
|
||||
// Compute attention scores: (Q * K^T + G_bias) / sqrt(d), then softmax.
|
||||
let mut attention_weights = vec![0.0_f32; n * n];
|
||||
for i in 0..n {
|
||||
// Compute raw scores for row i.
|
||||
let mut max_score = f32::NEG_INFINITY;
|
||||
for j in 0..n {
|
||||
let dot: f32 = queries[i].iter().zip(&keys[j]).map(|(q, k)| q * k).sum();
|
||||
let score = (dot + g_bias[i * n + j]) * scale;
|
||||
attention_weights[i * n + j] = score;
|
||||
if score > max_score {
|
||||
max_score = score;
|
||||
}
|
||||
}
|
||||
|
||||
// Softmax: subtract max for numerical stability, then exponentiate.
|
||||
let mut sum_exp = 0.0_f32;
|
||||
for j in 0..n {
|
||||
let val = (attention_weights[i * n + j] - max_score).exp();
|
||||
attention_weights[i * n + j] = val;
|
||||
sum_exp += val;
|
||||
}
|
||||
let safe_sum = sum_exp.max(f32::EPSILON);
|
||||
for j in 0..n {
|
||||
attention_weights[i * n + j] /= safe_sum;
|
||||
}
|
||||
}
|
||||
|
||||
// Weighted sum: attended[i] = sum_j (attention_weights[i,j] * values[j]).
|
||||
let mut attended = Vec::with_capacity(n);
|
||||
for i in 0..n {
|
||||
let mut output = vec![0.0_f32; d];
|
||||
for j in 0..n {
|
||||
let w = attention_weights[i * n + j];
|
||||
for k in 0..d {
|
||||
output[k] += w * values[j][k];
|
||||
}
|
||||
}
|
||||
attended.push(output);
|
||||
}
|
||||
|
||||
Ok(attended)
|
||||
}
|
||||
|
||||
/// Fuse multiple viewpoint embeddings into a single embedding.
|
||||
///
|
||||
/// Applies cross-viewpoint attention, then mean-pools the attended outputs
|
||||
/// to produce a single fused embedding of dimension `d_out`.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `embeddings`: per-viewpoint embedding vectors.
|
||||
/// - `viewpoint_geom`: per-viewpoint geometry descriptors.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// A single fused embedding of length `d_out`.
|
||||
pub fn fuse(
|
||||
&self,
|
||||
embeddings: &[Vec<f32>],
|
||||
viewpoint_geom: &[ViewpointGeometry],
|
||||
) -> Result<Vec<f32>, AttentionError> {
|
||||
let attended = self.attend(embeddings, viewpoint_geom)?;
|
||||
let n = attended.len();
|
||||
let d = self.weights.d_out;
|
||||
let mut fused = vec![0.0_f32; d];
|
||||
|
||||
for row in &attended {
|
||||
for k in 0..d {
|
||||
fused[k] += row[k];
|
||||
}
|
||||
}
|
||||
let n_f = n as f32;
|
||||
for k in 0..d {
|
||||
fused[k] /= n_f;
|
||||
}
|
||||
|
||||
Ok(fused)
|
||||
}
|
||||
|
||||
/// Extract the raw attention weight matrix (for diagnostics).
|
||||
///
|
||||
/// Returns the `N x N` attention weight matrix (row-major, each row sums to 1).
|
||||
pub fn attention_weights(
|
||||
&self,
|
||||
embeddings: &[Vec<f32>],
|
||||
viewpoint_geom: &[ViewpointGeometry],
|
||||
) -> Result<Vec<f32>, AttentionError> {
|
||||
let n = embeddings.len();
|
||||
if n == 0 {
|
||||
return Err(AttentionError::EmptyViewpoints);
|
||||
}
|
||||
|
||||
let d = self.weights.d_out;
|
||||
let scale = 1.0 / (d as f32).sqrt();
|
||||
|
||||
let queries = self.weights.project_queries(embeddings);
|
||||
let keys = self.weights.project_keys(embeddings);
|
||||
let g_bias = self.bias.build_matrix(viewpoint_geom);
|
||||
|
||||
let mut weights = vec![0.0_f32; n * n];
|
||||
for i in 0..n {
|
||||
let mut max_score = f32::NEG_INFINITY;
|
||||
for j in 0..n {
|
||||
let dot: f32 = queries[i].iter().zip(&keys[j]).map(|(q, k)| q * k).sum();
|
||||
let score = (dot + g_bias[i * n + j]) * scale;
|
||||
weights[i * n + j] = score;
|
||||
if score > max_score {
|
||||
max_score = score;
|
||||
}
|
||||
}
|
||||
|
||||
let mut sum_exp = 0.0_f32;
|
||||
for j in 0..n {
|
||||
let val = (weights[i * n + j] - max_score).exp();
|
||||
weights[i * n + j] = val;
|
||||
sum_exp += val;
|
||||
}
|
||||
let safe_sum = sum_exp.max(f32::EPSILON);
|
||||
for j in 0..n {
|
||||
weights[i * n + j] /= safe_sum;
|
||||
}
|
||||
}
|
||||
|
||||
Ok(weights)
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_test_geom(n: usize) -> Vec<ViewpointGeometry> {
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let angle = 2.0 * std::f32::consts::PI * i as f32 / n as f32;
|
||||
let r = 3.0;
|
||||
ViewpointGeometry {
|
||||
azimuth: angle,
|
||||
position: (r * angle.cos(), r * angle.sin()),
|
||||
}
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn make_test_embeddings(n: usize, dim: usize) -> Vec<Vec<f32>> {
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
(0..dim).map(|d| ((i * dim + d) as f32 * 0.01).sin()).collect()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_produces_correct_dimension() {
|
||||
let dim = 16;
|
||||
let n = 4;
|
||||
let attn = CrossViewpointAttention::new(dim);
|
||||
let embeddings = make_test_embeddings(n, dim);
|
||||
let geom = make_test_geom(n);
|
||||
let fused = attn.fuse(&embeddings, &geom).unwrap();
|
||||
assert_eq!(fused.len(), dim, "fused embedding must have length {dim}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn attend_produces_n_outputs() {
|
||||
let dim = 8;
|
||||
let n = 3;
|
||||
let attn = CrossViewpointAttention::new(dim);
|
||||
let embeddings = make_test_embeddings(n, dim);
|
||||
let geom = make_test_geom(n);
|
||||
let attended = attn.attend(&embeddings, &geom).unwrap();
|
||||
assert_eq!(attended.len(), n, "must produce one output per viewpoint");
|
||||
for row in &attended {
|
||||
assert_eq!(row.len(), dim);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn attention_weights_sum_to_one() {
|
||||
let dim = 8;
|
||||
let n = 4;
|
||||
let attn = CrossViewpointAttention::new(dim);
|
||||
let embeddings = make_test_embeddings(n, dim);
|
||||
let geom = make_test_geom(n);
|
||||
let weights = attn.attention_weights(&embeddings, &geom).unwrap();
|
||||
assert_eq!(weights.len(), n * n);
|
||||
for i in 0..n {
|
||||
let row_sum: f32 = (0..n).map(|j| weights[i * n + j]).sum();
|
||||
assert!(
|
||||
(row_sum - 1.0).abs() < 1e-5,
|
||||
"row {i} sums to {row_sum}, expected 1.0"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn attention_weights_are_non_negative() {
|
||||
let dim = 8;
|
||||
let n = 3;
|
||||
let attn = CrossViewpointAttention::new(dim);
|
||||
let embeddings = make_test_embeddings(n, dim);
|
||||
let geom = make_test_geom(n);
|
||||
let weights = attn.attention_weights(&embeddings, &geom).unwrap();
|
||||
for w in &weights {
|
||||
assert!(*w >= 0.0, "attention weight must be non-negative, got {w}");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_viewpoints_returns_error() {
|
||||
let attn = CrossViewpointAttention::new(8);
|
||||
let result = attn.fuse(&[], &[]);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dimension_mismatch_returns_error() {
|
||||
let attn = CrossViewpointAttention::new(8);
|
||||
let embeddings = vec![vec![1.0_f32; 4]]; // wrong dim
|
||||
let geom = make_test_geom(1);
|
||||
let result = attn.fuse(&embeddings, &geom);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn geometric_bias_pair_computation() {
|
||||
let bias = GeometricBias::new(1.0, 1.0, 5.0);
|
||||
// Same position: theta=0, d=0 -> cos(0) + exp(0) = 2.0
|
||||
let val = bias.compute_pair(0.0, 0.0);
|
||||
assert!((val - 2.0).abs() < 1e-5, "self-bias should be 2.0, got {val}");
|
||||
|
||||
// Orthogonal, far apart: theta=PI/2, d=5.0
|
||||
let val_orth = bias.compute_pair(std::f32::consts::FRAC_PI_2, 5.0);
|
||||
// cos(PI/2) ~ 0 + exp(-1) ~ 0.368
|
||||
assert!(val_orth < 1.0, "orthogonal far-apart viewpoints should have low bias");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn geometric_bias_matrix_is_symmetric_for_symmetric_layout() {
|
||||
let bias = GeometricBias::default();
|
||||
let geom = make_test_geom(4);
|
||||
let matrix = bias.build_matrix(&geom);
|
||||
let n = 4;
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
assert!(
|
||||
(matrix[i * n + j] - matrix[j * n + i]).abs() < 1e-5,
|
||||
"bias matrix must be symmetric for symmetric layout: [{i},{j}]={} vs [{j},{i}]={}",
|
||||
matrix[i * n + j],
|
||||
matrix[j * n + i]
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn single_viewpoint_fuse_returns_projection() {
|
||||
let dim = 8;
|
||||
let attn = CrossViewpointAttention::new(dim);
|
||||
let embeddings = vec![vec![1.0_f32; dim]];
|
||||
let geom = make_test_geom(1);
|
||||
let fused = attn.fuse(&embeddings, &geom).unwrap();
|
||||
// With identity projection and single viewpoint, fused == input.
|
||||
for (i, v) in fused.iter().enumerate() {
|
||||
assert!(
|
||||
(v - 1.0).abs() < 1e-5,
|
||||
"single-viewpoint fuse should return input, dim {i}: {v}"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn projection_weights_custom_transform() {
|
||||
// Verify that non-identity weights change the output.
|
||||
let dim = 4;
|
||||
// Swap first two dimensions in Q.
|
||||
let mut w_q = vec![0.0_f32; dim * dim];
|
||||
w_q[0 * dim + 1] = 1.0; // row 0 picks dim 1
|
||||
w_q[1 * dim + 0] = 1.0; // row 1 picks dim 0
|
||||
w_q[2 * dim + 2] = 1.0;
|
||||
w_q[3 * dim + 3] = 1.0;
|
||||
let w_id = {
|
||||
let mut eye = vec![0.0_f32; dim * dim];
|
||||
for i in 0..dim {
|
||||
eye[i * dim + i] = 1.0;
|
||||
}
|
||||
eye
|
||||
};
|
||||
let weights = ProjectionWeights::new(w_q, w_id.clone(), w_id, dim, dim).unwrap();
|
||||
let queries = weights.project_queries(&[vec![1.0, 2.0, 3.0, 4.0]]);
|
||||
assert_eq!(queries[0], vec![2.0, 1.0, 3.0, 4.0]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn geometric_bias_with_large_distance_decays() {
|
||||
let bias = GeometricBias::new(0.0, 1.0, 2.0); // only distance component
|
||||
let close = bias.compute_pair(0.0, 0.5);
|
||||
let far = bias.compute_pair(0.0, 10.0);
|
||||
assert!(close > far, "closer viewpoints should have higher distance bias");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,383 @@
|
||||
//! Coherence gating for environment stability (ADR-031).
|
||||
//!
|
||||
//! Phase coherence determines whether the wireless environment is sufficiently
|
||||
//! stable for a model update. When multipath conditions change rapidly (e.g.
|
||||
//! doors opening, people entering), phase becomes incoherent and fusion
|
||||
//! quality degrades. The coherence gate prevents model updates during these
|
||||
//! transient periods.
|
||||
//!
|
||||
//! The core computation is the complex mean of unit phasors:
|
||||
//!
|
||||
//! ```text
|
||||
//! coherence = |mean(exp(j * delta_phi))|
|
||||
//! = sqrt((mean(cos(delta_phi)))^2 + (mean(sin(delta_phi)))^2)
|
||||
//! ```
|
||||
//!
|
||||
//! A coherence value near 1.0 indicates consistent phase; near 0.0 indicates
|
||||
//! random phase (incoherent environment).
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// CoherenceState
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Rolling coherence state tracking phase consistency over a sliding window.
|
||||
///
|
||||
/// Maintains a circular buffer of phase differences and incrementally updates
|
||||
/// the coherence estimate as new measurements arrive.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CoherenceState {
|
||||
/// Circular buffer of phase differences (radians).
|
||||
phase_diffs: Vec<f32>,
|
||||
/// Write position in the circular buffer.
|
||||
write_pos: usize,
|
||||
/// Number of valid entries in the buffer (may be less than capacity
|
||||
/// during warm-up).
|
||||
count: usize,
|
||||
/// Running sum of cos(phase_diff).
|
||||
sum_cos: f64,
|
||||
/// Running sum of sin(phase_diff).
|
||||
sum_sin: f64,
|
||||
}
|
||||
|
||||
impl CoherenceState {
|
||||
/// Create a new coherence state with the given window size.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `window_size`: number of phase measurements to retain. Larger windows
|
||||
/// are more stable but respond more slowly to environment changes.
|
||||
/// Must be at least 1.
|
||||
pub fn new(window_size: usize) -> Self {
|
||||
let size = window_size.max(1);
|
||||
CoherenceState {
|
||||
phase_diffs: vec![0.0; size],
|
||||
write_pos: 0,
|
||||
count: 0,
|
||||
sum_cos: 0.0,
|
||||
sum_sin: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Push a new phase difference measurement into the rolling window.
|
||||
///
|
||||
/// If the buffer is full, the oldest measurement is evicted and its
|
||||
/// contribution is subtracted from the running sums.
|
||||
pub fn push(&mut self, phase_diff: f32) {
|
||||
let cap = self.phase_diffs.len();
|
||||
|
||||
// If buffer is full, subtract the evicted entry.
|
||||
if self.count == cap {
|
||||
let old = self.phase_diffs[self.write_pos];
|
||||
self.sum_cos -= old.cos() as f64;
|
||||
self.sum_sin -= old.sin() as f64;
|
||||
} else {
|
||||
self.count += 1;
|
||||
}
|
||||
|
||||
// Write new entry.
|
||||
self.phase_diffs[self.write_pos] = phase_diff;
|
||||
self.sum_cos += phase_diff.cos() as f64;
|
||||
self.sum_sin += phase_diff.sin() as f64;
|
||||
|
||||
self.write_pos = (self.write_pos + 1) % cap;
|
||||
}
|
||||
|
||||
/// Current coherence value in `[0, 1]`.
|
||||
///
|
||||
/// Returns 0.0 if no measurements have been pushed yet.
|
||||
pub fn coherence(&self) -> f32 {
|
||||
if self.count == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
let n = self.count as f64;
|
||||
let mean_cos = self.sum_cos / n;
|
||||
let mean_sin = self.sum_sin / n;
|
||||
(mean_cos * mean_cos + mean_sin * mean_sin).sqrt() as f32
|
||||
}
|
||||
|
||||
/// Number of measurements currently in the buffer.
|
||||
pub fn len(&self) -> usize {
|
||||
self.count
|
||||
}
|
||||
|
||||
/// Returns `true` if no measurements have been pushed.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.count == 0
|
||||
}
|
||||
|
||||
/// Window capacity.
|
||||
pub fn capacity(&self) -> usize {
|
||||
self.phase_diffs.len()
|
||||
}
|
||||
|
||||
/// Reset the coherence state, clearing all measurements.
|
||||
pub fn reset(&mut self) {
|
||||
self.write_pos = 0;
|
||||
self.count = 0;
|
||||
self.sum_cos = 0.0;
|
||||
self.sum_sin = 0.0;
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// CoherenceGate
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Coherence gate that controls model updates based on phase stability.
|
||||
///
|
||||
/// Only allows model updates when the coherence exceeds a configurable
|
||||
/// threshold. Provides hysteresis to avoid rapid gate toggling near the
|
||||
/// threshold boundary.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CoherenceGate {
|
||||
/// Coherence threshold for opening the gate.
|
||||
pub threshold: f32,
|
||||
/// Hysteresis band: gate opens at `threshold` and closes at
|
||||
/// `threshold - hysteresis`.
|
||||
pub hysteresis: f32,
|
||||
/// Current gate state: `true` = open (updates allowed).
|
||||
gate_open: bool,
|
||||
/// Total number of gate evaluations.
|
||||
total_evaluations: u64,
|
||||
/// Number of times the gate was open.
|
||||
open_count: u64,
|
||||
}
|
||||
|
||||
impl CoherenceGate {
|
||||
/// Create a new coherence gate with the given threshold.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `threshold`: coherence level required for the gate to open (typically 0.7).
|
||||
/// - `hysteresis`: band below the threshold where the gate stays in its
|
||||
/// current state (typically 0.05).
|
||||
pub fn new(threshold: f32, hysteresis: f32) -> Self {
|
||||
CoherenceGate {
|
||||
threshold: threshold.clamp(0.0, 1.0),
|
||||
hysteresis: hysteresis.clamp(0.0, threshold),
|
||||
gate_open: false,
|
||||
total_evaluations: 0,
|
||||
open_count: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a gate with default parameters (threshold=0.7, hysteresis=0.05).
|
||||
pub fn default_params() -> Self {
|
||||
Self::new(0.7, 0.05)
|
||||
}
|
||||
|
||||
/// Evaluate the gate against the current coherence value.
|
||||
///
|
||||
/// Returns `true` if the gate is open (model update allowed).
|
||||
pub fn evaluate(&mut self, coherence: f32) -> bool {
|
||||
self.total_evaluations += 1;
|
||||
|
||||
if self.gate_open {
|
||||
// Gate is open: close if coherence drops below threshold - hysteresis.
|
||||
if coherence < self.threshold - self.hysteresis {
|
||||
self.gate_open = false;
|
||||
}
|
||||
} else {
|
||||
// Gate is closed: open if coherence exceeds threshold.
|
||||
if coherence >= self.threshold {
|
||||
self.gate_open = true;
|
||||
}
|
||||
}
|
||||
|
||||
if self.gate_open {
|
||||
self.open_count += 1;
|
||||
}
|
||||
|
||||
self.gate_open
|
||||
}
|
||||
|
||||
/// Whether the gate is currently open.
|
||||
pub fn is_open(&self) -> bool {
|
||||
self.gate_open
|
||||
}
|
||||
|
||||
/// Fraction of evaluations where the gate was open.
|
||||
pub fn duty_cycle(&self) -> f32 {
|
||||
if self.total_evaluations == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
self.open_count as f32 / self.total_evaluations as f32
|
||||
}
|
||||
|
||||
/// Reset the gate state and counters.
|
||||
pub fn reset(&mut self) {
|
||||
self.gate_open = false;
|
||||
self.total_evaluations = 0;
|
||||
self.open_count = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/// Stateless coherence gate function matching the ADR-031 specification.
|
||||
///
|
||||
/// Computes the complex mean of unit phasors from the given phase differences
|
||||
/// and returns `true` when coherence exceeds the threshold.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `phase_diffs`: delta-phi over T recent frames (radians).
|
||||
/// - `threshold`: coherence threshold (typically 0.7).
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `true` if the phase coherence exceeds the threshold.
|
||||
pub fn coherence_gate(phase_diffs: &[f32], threshold: f32) -> bool {
|
||||
if phase_diffs.is_empty() {
|
||||
return false;
|
||||
}
|
||||
let (sum_cos, sum_sin) = phase_diffs
|
||||
.iter()
|
||||
.fold((0.0_f32, 0.0_f32), |(c, s), &dp| {
|
||||
(c + dp.cos(), s + dp.sin())
|
||||
});
|
||||
let n = phase_diffs.len() as f32;
|
||||
let coherence = ((sum_cos / n).powi(2) + (sum_sin / n).powi(2)).sqrt();
|
||||
coherence > threshold
|
||||
}
|
||||
|
||||
/// Compute the raw coherence value from phase differences.
|
||||
///
|
||||
/// Returns a value in `[0, 1]` where 1.0 = perfectly coherent phase.
|
||||
pub fn compute_coherence(phase_diffs: &[f32]) -> f32 {
|
||||
if phase_diffs.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
let (sum_cos, sum_sin) = phase_diffs
|
||||
.iter()
|
||||
.fold((0.0_f32, 0.0_f32), |(c, s), &dp| {
|
||||
(c + dp.cos(), s + dp.sin())
|
||||
});
|
||||
let n = phase_diffs.len() as f32;
|
||||
((sum_cos / n).powi(2) + (sum_sin / n).powi(2)).sqrt()
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn coherent_phase_returns_high_value() {
|
||||
// All phase diffs are the same -> coherence ~ 1.0
|
||||
let phase_diffs = vec![0.5_f32; 100];
|
||||
let c = compute_coherence(&phase_diffs);
|
||||
assert!(c > 0.99, "identical phases should give coherence ~ 1.0, got {c}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn random_phase_returns_low_value() {
|
||||
// Uniformly spaced phases around the circle -> coherence ~ 0.0
|
||||
let n = 1000;
|
||||
let phase_diffs: Vec<f32> = (0..n)
|
||||
.map(|i| 2.0 * std::f32::consts::PI * i as f32 / n as f32)
|
||||
.collect();
|
||||
let c = compute_coherence(&phase_diffs);
|
||||
assert!(c < 0.05, "uniformly spread phases should give coherence ~ 0.0, got {c}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_gate_opens_above_threshold() {
|
||||
let coherent = vec![0.3_f32; 50]; // same phase -> high coherence
|
||||
assert!(coherence_gate(&coherent, 0.7));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_gate_closed_below_threshold() {
|
||||
let n = 500;
|
||||
let incoherent: Vec<f32> = (0..n)
|
||||
.map(|i| 2.0 * std::f32::consts::PI * i as f32 / n as f32)
|
||||
.collect();
|
||||
assert!(!coherence_gate(&incoherent, 0.7));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_gate_empty_returns_false() {
|
||||
assert!(!coherence_gate(&[], 0.5));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_state_rolling_window() {
|
||||
let mut state = CoherenceState::new(10);
|
||||
// Push coherent measurements.
|
||||
for _ in 0..10 {
|
||||
state.push(1.0);
|
||||
}
|
||||
let c1 = state.coherence();
|
||||
assert!(c1 > 0.9, "coherent window should give high coherence");
|
||||
|
||||
// Push incoherent measurements to replace the window.
|
||||
for i in 0..10 {
|
||||
state.push(i as f32 * 0.628);
|
||||
}
|
||||
let c2 = state.coherence();
|
||||
assert!(c2 < c1, "incoherent updates should reduce coherence");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_state_empty_returns_zero() {
|
||||
let state = CoherenceState::new(10);
|
||||
assert_eq!(state.coherence(), 0.0);
|
||||
assert!(state.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gate_hysteresis_prevents_toggling() {
|
||||
let mut gate = CoherenceGate::new(0.7, 0.1);
|
||||
// Open the gate.
|
||||
assert!(gate.evaluate(0.8));
|
||||
assert!(gate.is_open());
|
||||
|
||||
// Coherence drops to 0.65 (below threshold but within hysteresis band).
|
||||
assert!(gate.evaluate(0.65));
|
||||
assert!(gate.is_open(), "gate should stay open within hysteresis band");
|
||||
|
||||
// Coherence drops below hysteresis boundary (0.7 - 0.1 = 0.6).
|
||||
assert!(!gate.evaluate(0.55));
|
||||
assert!(!gate.is_open(), "gate should close below hysteresis boundary");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gate_duty_cycle_tracks_correctly() {
|
||||
let mut gate = CoherenceGate::new(0.5, 0.0);
|
||||
gate.evaluate(0.6); // open
|
||||
gate.evaluate(0.6); // open
|
||||
gate.evaluate(0.3); // close
|
||||
gate.evaluate(0.3); // close
|
||||
let duty = gate.duty_cycle();
|
||||
assert!(
|
||||
(duty - 0.5).abs() < 1e-5,
|
||||
"duty cycle should be 0.5, got {duty}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gate_reset_clears_state() {
|
||||
let mut gate = CoherenceGate::new(0.5, 0.0);
|
||||
gate.evaluate(0.6);
|
||||
assert!(gate.is_open());
|
||||
gate.reset();
|
||||
assert!(!gate.is_open());
|
||||
assert_eq!(gate.duty_cycle(), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_state_push_and_len() {
|
||||
let mut state = CoherenceState::new(5);
|
||||
assert_eq!(state.len(), 0);
|
||||
state.push(0.1);
|
||||
state.push(0.2);
|
||||
assert_eq!(state.len(), 2);
|
||||
// Fill past capacity.
|
||||
for i in 0..10 {
|
||||
state.push(i as f32 * 0.1);
|
||||
}
|
||||
assert_eq!(state.len(), 5, "count should be capped at window size");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,696 @@
|
||||
//! MultistaticArray aggregate root and fusion pipeline orchestrator (ADR-031).
|
||||
//!
|
||||
//! [`MultistaticArray`] is the DDD aggregate root for the ViewpointFusion
|
||||
//! bounded context. It orchestrates the full fusion pipeline:
|
||||
//!
|
||||
//! 1. Collect per-viewpoint AETHER embeddings.
|
||||
//! 2. Compute geometric bias from viewpoint pair geometry.
|
||||
//! 3. Apply cross-viewpoint attention with geometric bias.
|
||||
//! 4. Gate the output through coherence check.
|
||||
//! 5. Emit a fused embedding for the DensePose regression head.
|
||||
//!
|
||||
//! Uses `ruvector-attention` for the attention mechanism and
|
||||
//! `ruvector-attn-mincut` for optional noise gating on embeddings.
|
||||
|
||||
use crate::viewpoint::attention::{
|
||||
AttentionError, CrossViewpointAttention, GeometricBias, ViewpointGeometry,
|
||||
};
|
||||
use crate::viewpoint::coherence::{CoherenceGate, CoherenceState};
|
||||
use crate::viewpoint::geometry::{GeometricDiversityIndex, NodeId};
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Domain types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Unique identifier for a multistatic array deployment.
|
||||
pub type ArrayId = u64;
|
||||
|
||||
/// Per-viewpoint embedding with geometric metadata.
|
||||
///
|
||||
/// Represents a single CSI observation processed through the per-viewpoint
|
||||
/// signal pipeline and AETHER encoder into a contrastive embedding.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ViewpointEmbedding {
|
||||
/// Source node identifier.
|
||||
pub node_id: NodeId,
|
||||
/// AETHER embedding vector (typically 128-d).
|
||||
pub embedding: Vec<f32>,
|
||||
/// Azimuth angle from array centroid (radians).
|
||||
pub azimuth: f32,
|
||||
/// Elevation angle (radians, 0 for 2-D deployments).
|
||||
pub elevation: f32,
|
||||
/// Baseline distance from array centroid (metres).
|
||||
pub baseline: f32,
|
||||
/// Node position in metres (x, y).
|
||||
pub position: (f32, f32),
|
||||
/// Signal-to-noise ratio at capture time (dB).
|
||||
pub snr_db: f32,
|
||||
}
|
||||
|
||||
/// Fused embedding output from the cross-viewpoint attention pipeline.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct FusedEmbedding {
|
||||
/// The fused embedding vector.
|
||||
pub embedding: Vec<f32>,
|
||||
/// Geometric Diversity Index at the time of fusion.
|
||||
pub gdi: f32,
|
||||
/// Coherence value at the time of fusion.
|
||||
pub coherence: f32,
|
||||
/// Number of viewpoints that contributed to the fusion.
|
||||
pub n_viewpoints: usize,
|
||||
/// Effective independent viewpoints (after correlation discount).
|
||||
pub n_effective: f32,
|
||||
}
|
||||
|
||||
/// Configuration for the fusion pipeline.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct FusionConfig {
|
||||
/// Embedding dimension (must match AETHER output, typically 128).
|
||||
pub embed_dim: usize,
|
||||
/// Coherence threshold for gating (typically 0.7).
|
||||
pub coherence_threshold: f32,
|
||||
/// Coherence hysteresis band (typically 0.05).
|
||||
pub coherence_hysteresis: f32,
|
||||
/// Coherence rolling window size (number of frames).
|
||||
pub coherence_window: usize,
|
||||
/// Geometric bias angle weight.
|
||||
pub w_angle: f32,
|
||||
/// Geometric bias distance weight.
|
||||
pub w_dist: f32,
|
||||
/// Reference distance for geometric bias decay (metres).
|
||||
pub d_ref: f32,
|
||||
/// Minimum SNR (dB) for a viewpoint to contribute to fusion.
|
||||
pub min_snr_db: f32,
|
||||
}
|
||||
|
||||
impl Default for FusionConfig {
|
||||
fn default() -> Self {
|
||||
FusionConfig {
|
||||
embed_dim: 128,
|
||||
coherence_threshold: 0.7,
|
||||
coherence_hysteresis: 0.05,
|
||||
coherence_window: 50,
|
||||
w_angle: 1.0,
|
||||
w_dist: 1.0,
|
||||
d_ref: 5.0,
|
||||
min_snr_db: 5.0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Fusion errors
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors produced by the fusion pipeline.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum FusionError {
|
||||
/// No viewpoint embeddings available for fusion.
|
||||
NoViewpoints,
|
||||
/// All viewpoints were filtered out (e.g. by SNR threshold).
|
||||
AllFiltered {
|
||||
/// Number of viewpoints that were rejected.
|
||||
rejected: usize,
|
||||
},
|
||||
/// Coherence gate is closed (environment too unstable).
|
||||
CoherenceGateClosed {
|
||||
/// Current coherence value.
|
||||
coherence: f32,
|
||||
/// Required threshold.
|
||||
threshold: f32,
|
||||
},
|
||||
/// Internal attention computation error.
|
||||
AttentionError(AttentionError),
|
||||
/// Embedding dimension mismatch.
|
||||
DimensionMismatch {
|
||||
/// Expected dimension.
|
||||
expected: usize,
|
||||
/// Actual dimension.
|
||||
actual: usize,
|
||||
/// Node that produced the mismatched embedding.
|
||||
node_id: NodeId,
|
||||
},
|
||||
}
|
||||
|
||||
impl std::fmt::Display for FusionError {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
match self {
|
||||
FusionError::NoViewpoints => write!(f, "no viewpoint embeddings available"),
|
||||
FusionError::AllFiltered { rejected } => {
|
||||
write!(f, "all {rejected} viewpoints filtered by SNR threshold")
|
||||
}
|
||||
FusionError::CoherenceGateClosed { coherence, threshold } => {
|
||||
write!(
|
||||
f,
|
||||
"coherence gate closed: coherence={coherence:.3} < threshold={threshold:.3}"
|
||||
)
|
||||
}
|
||||
FusionError::AttentionError(e) => write!(f, "attention error: {e}"),
|
||||
FusionError::DimensionMismatch { expected, actual, node_id } => {
|
||||
write!(
|
||||
f,
|
||||
"node {node_id} embedding dim {actual} != expected {expected}"
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl std::error::Error for FusionError {}
|
||||
|
||||
impl From<AttentionError> for FusionError {
|
||||
fn from(e: AttentionError) -> Self {
|
||||
FusionError::AttentionError(e)
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Domain events
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Events emitted by the ViewpointFusion aggregate.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum ViewpointFusionEvent {
|
||||
/// A viewpoint embedding was received from a node.
|
||||
ViewpointCaptured {
|
||||
/// Source node.
|
||||
node_id: NodeId,
|
||||
/// Signal quality.
|
||||
snr_db: f32,
|
||||
},
|
||||
/// A TDM cycle completed with all (or some) viewpoints received.
|
||||
TdmCycleCompleted {
|
||||
/// Monotonic cycle counter.
|
||||
cycle_id: u64,
|
||||
/// Number of viewpoints received this cycle.
|
||||
viewpoints_received: usize,
|
||||
},
|
||||
/// Fusion completed successfully.
|
||||
FusionCompleted {
|
||||
/// GDI at the time of fusion.
|
||||
gdi: f32,
|
||||
/// Number of viewpoints fused.
|
||||
n_viewpoints: usize,
|
||||
},
|
||||
/// Coherence gate evaluation result.
|
||||
CoherenceGateTriggered {
|
||||
/// Current coherence value.
|
||||
coherence: f32,
|
||||
/// Whether the gate accepted the update.
|
||||
accepted: bool,
|
||||
},
|
||||
/// Array geometry was updated.
|
||||
GeometryUpdated {
|
||||
/// New GDI value.
|
||||
new_gdi: f32,
|
||||
/// Effective independent viewpoints.
|
||||
n_effective: f32,
|
||||
},
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// MultistaticArray (aggregate root)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Aggregate root for the ViewpointFusion bounded context.
|
||||
///
|
||||
/// Manages the lifecycle of a multistatic sensor array: collecting viewpoint
|
||||
/// embeddings, computing geometric diversity, gating on coherence, and
|
||||
/// producing fused embeddings for downstream pose estimation.
|
||||
pub struct MultistaticArray {
|
||||
/// Unique deployment identifier.
|
||||
id: ArrayId,
|
||||
/// Active viewpoint embeddings (latest per node).
|
||||
viewpoints: Vec<ViewpointEmbedding>,
|
||||
/// Cross-viewpoint attention module.
|
||||
attention: CrossViewpointAttention,
|
||||
/// Coherence state tracker.
|
||||
coherence_state: CoherenceState,
|
||||
/// Coherence gate.
|
||||
coherence_gate: CoherenceGate,
|
||||
/// Pipeline configuration.
|
||||
config: FusionConfig,
|
||||
/// Monotonic TDM cycle counter.
|
||||
cycle_count: u64,
|
||||
/// Event log (bounded).
|
||||
events: Vec<ViewpointFusionEvent>,
|
||||
/// Maximum events to retain.
|
||||
max_events: usize,
|
||||
}
|
||||
|
||||
impl MultistaticArray {
|
||||
/// Create a new multistatic array with the given configuration.
|
||||
pub fn new(id: ArrayId, config: FusionConfig) -> Self {
|
||||
let attention = CrossViewpointAttention::new(config.embed_dim);
|
||||
let attention = CrossViewpointAttention::with_params(
|
||||
attention.weights,
|
||||
GeometricBias::new(config.w_angle, config.w_dist, config.d_ref),
|
||||
);
|
||||
let coherence_state = CoherenceState::new(config.coherence_window);
|
||||
let coherence_gate =
|
||||
CoherenceGate::new(config.coherence_threshold, config.coherence_hysteresis);
|
||||
|
||||
MultistaticArray {
|
||||
id,
|
||||
viewpoints: Vec::new(),
|
||||
attention,
|
||||
coherence_state,
|
||||
coherence_gate,
|
||||
config,
|
||||
cycle_count: 0,
|
||||
events: Vec::new(),
|
||||
max_events: 1000,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create with default configuration.
|
||||
pub fn with_defaults(id: ArrayId) -> Self {
|
||||
Self::new(id, FusionConfig::default())
|
||||
}
|
||||
|
||||
/// Array deployment identifier.
|
||||
pub fn id(&self) -> ArrayId {
|
||||
self.id
|
||||
}
|
||||
|
||||
/// Number of viewpoints currently held.
|
||||
pub fn n_viewpoints(&self) -> usize {
|
||||
self.viewpoints.len()
|
||||
}
|
||||
|
||||
/// Current TDM cycle count.
|
||||
pub fn cycle_count(&self) -> u64 {
|
||||
self.cycle_count
|
||||
}
|
||||
|
||||
/// Submit a viewpoint embedding from a sensor node.
|
||||
///
|
||||
/// Replaces any existing embedding for the same `node_id`.
|
||||
pub fn submit_viewpoint(&mut self, vp: ViewpointEmbedding) -> Result<(), FusionError> {
|
||||
// Validate embedding dimension.
|
||||
if vp.embedding.len() != self.config.embed_dim {
|
||||
return Err(FusionError::DimensionMismatch {
|
||||
expected: self.config.embed_dim,
|
||||
actual: vp.embedding.len(),
|
||||
node_id: vp.node_id,
|
||||
});
|
||||
}
|
||||
|
||||
self.emit_event(ViewpointFusionEvent::ViewpointCaptured {
|
||||
node_id: vp.node_id,
|
||||
snr_db: vp.snr_db,
|
||||
});
|
||||
|
||||
// Upsert: replace existing embedding for this node.
|
||||
if let Some(pos) = self.viewpoints.iter().position(|v| v.node_id == vp.node_id) {
|
||||
self.viewpoints[pos] = vp;
|
||||
} else {
|
||||
self.viewpoints.push(vp);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Push a phase-difference measurement for coherence tracking.
|
||||
pub fn push_phase_diff(&mut self, phase_diff: f32) {
|
||||
self.coherence_state.push(phase_diff);
|
||||
}
|
||||
|
||||
/// Current coherence value.
|
||||
pub fn coherence(&self) -> f32 {
|
||||
self.coherence_state.coherence()
|
||||
}
|
||||
|
||||
/// Compute the Geometric Diversity Index for the current array layout.
|
||||
pub fn compute_gdi(&self) -> Option<GeometricDiversityIndex> {
|
||||
let azimuths: Vec<f32> = self.viewpoints.iter().map(|v| v.azimuth).collect();
|
||||
let ids: Vec<NodeId> = self.viewpoints.iter().map(|v| v.node_id).collect();
|
||||
let gdi = GeometricDiversityIndex::compute(&azimuths, &ids);
|
||||
if let Some(ref g) = gdi {
|
||||
// Emit event (mutable borrow not possible here, caller can do it).
|
||||
let _ = g; // used for return
|
||||
}
|
||||
gdi
|
||||
}
|
||||
|
||||
/// Run the full fusion pipeline.
|
||||
///
|
||||
/// 1. Filter viewpoints by SNR.
|
||||
/// 2. Check coherence gate.
|
||||
/// 3. Compute geometric bias.
|
||||
/// 4. Apply cross-viewpoint attention.
|
||||
/// 5. Mean-pool to single fused embedding.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `Ok(FusedEmbedding)` on success, or an error if the pipeline cannot
|
||||
/// produce a valid fusion (no viewpoints, gate closed, etc.).
|
||||
pub fn fuse(&mut self) -> Result<FusedEmbedding, FusionError> {
|
||||
self.cycle_count += 1;
|
||||
|
||||
// Extract all needed data from viewpoints upfront to avoid borrow conflicts.
|
||||
let min_snr = self.config.min_snr_db;
|
||||
let total_viewpoints = self.viewpoints.len();
|
||||
let extracted: Vec<(NodeId, Vec<f32>, f32, (f32, f32))> = self
|
||||
.viewpoints
|
||||
.iter()
|
||||
.filter(|v| v.snr_db >= min_snr)
|
||||
.map(|v| (v.node_id, v.embedding.clone(), v.azimuth, v.position))
|
||||
.collect();
|
||||
|
||||
let n_valid = extracted.len();
|
||||
if n_valid == 0 {
|
||||
if total_viewpoints == 0 {
|
||||
return Err(FusionError::NoViewpoints);
|
||||
}
|
||||
return Err(FusionError::AllFiltered {
|
||||
rejected: total_viewpoints,
|
||||
});
|
||||
}
|
||||
|
||||
// Check coherence gate.
|
||||
let coh = self.coherence_state.coherence();
|
||||
let gate_open = self.coherence_gate.evaluate(coh);
|
||||
|
||||
self.emit_event(ViewpointFusionEvent::CoherenceGateTriggered {
|
||||
coherence: coh,
|
||||
accepted: gate_open,
|
||||
});
|
||||
|
||||
if !gate_open {
|
||||
return Err(FusionError::CoherenceGateClosed {
|
||||
coherence: coh,
|
||||
threshold: self.config.coherence_threshold,
|
||||
});
|
||||
}
|
||||
|
||||
// Prepare embeddings and geometries from extracted data.
|
||||
let embeddings: Vec<Vec<f32>> = extracted.iter().map(|(_, e, _, _)| e.clone()).collect();
|
||||
let geom: Vec<ViewpointGeometry> = extracted
|
||||
.iter()
|
||||
.map(|(_, _, az, pos)| ViewpointGeometry {
|
||||
azimuth: *az,
|
||||
position: *pos,
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Run cross-viewpoint attention fusion.
|
||||
let fused_emb = self.attention.fuse(&embeddings, &geom)?;
|
||||
|
||||
// Compute GDI.
|
||||
let azimuths: Vec<f32> = extracted.iter().map(|(_, _, az, _)| *az).collect();
|
||||
let ids: Vec<NodeId> = extracted.iter().map(|(id, _, _, _)| *id).collect();
|
||||
let gdi_opt = GeometricDiversityIndex::compute(&azimuths, &ids);
|
||||
let (gdi_val, n_eff) = match &gdi_opt {
|
||||
Some(g) => (g.value, g.n_effective),
|
||||
None => (0.0, n_valid as f32),
|
||||
};
|
||||
|
||||
self.emit_event(ViewpointFusionEvent::TdmCycleCompleted {
|
||||
cycle_id: self.cycle_count,
|
||||
viewpoints_received: n_valid,
|
||||
});
|
||||
|
||||
self.emit_event(ViewpointFusionEvent::FusionCompleted {
|
||||
gdi: gdi_val,
|
||||
n_viewpoints: n_valid,
|
||||
});
|
||||
|
||||
Ok(FusedEmbedding {
|
||||
embedding: fused_emb,
|
||||
gdi: gdi_val,
|
||||
coherence: coh,
|
||||
n_viewpoints: n_valid,
|
||||
n_effective: n_eff,
|
||||
})
|
||||
}
|
||||
|
||||
/// Run fusion without coherence gating (for testing or forced updates).
|
||||
pub fn fuse_ungated(&mut self) -> Result<FusedEmbedding, FusionError> {
|
||||
let min_snr = self.config.min_snr_db;
|
||||
let total_viewpoints = self.viewpoints.len();
|
||||
let extracted: Vec<(NodeId, Vec<f32>, f32, (f32, f32))> = self
|
||||
.viewpoints
|
||||
.iter()
|
||||
.filter(|v| v.snr_db >= min_snr)
|
||||
.map(|v| (v.node_id, v.embedding.clone(), v.azimuth, v.position))
|
||||
.collect();
|
||||
|
||||
let n_valid = extracted.len();
|
||||
if n_valid == 0 {
|
||||
if total_viewpoints == 0 {
|
||||
return Err(FusionError::NoViewpoints);
|
||||
}
|
||||
return Err(FusionError::AllFiltered {
|
||||
rejected: total_viewpoints,
|
||||
});
|
||||
}
|
||||
|
||||
let embeddings: Vec<Vec<f32>> = extracted.iter().map(|(_, e, _, _)| e.clone()).collect();
|
||||
let geom: Vec<ViewpointGeometry> = extracted
|
||||
.iter()
|
||||
.map(|(_, _, az, pos)| ViewpointGeometry {
|
||||
azimuth: *az,
|
||||
position: *pos,
|
||||
})
|
||||
.collect();
|
||||
|
||||
let fused_emb = self.attention.fuse(&embeddings, &geom)?;
|
||||
|
||||
let azimuths: Vec<f32> = extracted.iter().map(|(_, _, az, _)| *az).collect();
|
||||
let ids: Vec<NodeId> = extracted.iter().map(|(id, _, _, _)| *id).collect();
|
||||
let gdi_opt = GeometricDiversityIndex::compute(&azimuths, &ids);
|
||||
let (gdi_val, n_eff) = match &gdi_opt {
|
||||
Some(g) => (g.value, g.n_effective),
|
||||
None => (0.0, n_valid as f32),
|
||||
};
|
||||
|
||||
let coh = self.coherence_state.coherence();
|
||||
|
||||
Ok(FusedEmbedding {
|
||||
embedding: fused_emb,
|
||||
gdi: gdi_val,
|
||||
coherence: coh,
|
||||
n_viewpoints: n_valid,
|
||||
n_effective: n_eff,
|
||||
})
|
||||
}
|
||||
|
||||
/// Access the event log.
|
||||
pub fn events(&self) -> &[ViewpointFusionEvent] {
|
||||
&self.events
|
||||
}
|
||||
|
||||
/// Clear the event log.
|
||||
pub fn clear_events(&mut self) {
|
||||
self.events.clear();
|
||||
}
|
||||
|
||||
/// Remove a viewpoint by node ID.
|
||||
pub fn remove_viewpoint(&mut self, node_id: NodeId) {
|
||||
self.viewpoints.retain(|v| v.node_id != node_id);
|
||||
}
|
||||
|
||||
/// Clear all viewpoints.
|
||||
pub fn clear_viewpoints(&mut self) {
|
||||
self.viewpoints.clear();
|
||||
}
|
||||
|
||||
fn emit_event(&mut self, event: ViewpointFusionEvent) {
|
||||
if self.events.len() >= self.max_events {
|
||||
// Drop oldest half to avoid unbounded growth.
|
||||
let half = self.max_events / 2;
|
||||
self.events.drain(..half);
|
||||
}
|
||||
self.events.push(event);
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_viewpoint(node_id: NodeId, angle_idx: usize, n: usize, dim: usize) -> ViewpointEmbedding {
|
||||
let angle = 2.0 * std::f32::consts::PI * angle_idx as f32 / n as f32;
|
||||
let r = 3.0;
|
||||
ViewpointEmbedding {
|
||||
node_id,
|
||||
embedding: (0..dim).map(|d| ((node_id as usize * dim + d) as f32 * 0.01).sin()).collect(),
|
||||
azimuth: angle,
|
||||
elevation: 0.0,
|
||||
baseline: r,
|
||||
position: (r * angle.cos(), r * angle.sin()),
|
||||
snr_db: 15.0,
|
||||
}
|
||||
}
|
||||
|
||||
fn setup_coherent_array(dim: usize) -> MultistaticArray {
|
||||
let config = FusionConfig {
|
||||
embed_dim: dim,
|
||||
coherence_threshold: 0.5,
|
||||
coherence_hysteresis: 0.0,
|
||||
min_snr_db: 0.0,
|
||||
..FusionConfig::default()
|
||||
};
|
||||
let mut array = MultistaticArray::new(1, config);
|
||||
// Push coherent phase diffs to open the gate.
|
||||
for _ in 0..60 {
|
||||
array.push_phase_diff(0.1);
|
||||
}
|
||||
array
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_produces_correct_dimension() {
|
||||
let dim = 16;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
for i in 0..4 {
|
||||
array.submit_viewpoint(make_viewpoint(i, i as usize, 4, dim)).unwrap();
|
||||
}
|
||||
let fused = array.fuse().unwrap();
|
||||
assert_eq!(fused.embedding.len(), dim);
|
||||
assert_eq!(fused.n_viewpoints, 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_no_viewpoints_returns_error() {
|
||||
let mut array = setup_coherent_array(16);
|
||||
assert!(matches!(array.fuse(), Err(FusionError::NoViewpoints)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_coherence_gate_closed_returns_error() {
|
||||
let dim = 16;
|
||||
let config = FusionConfig {
|
||||
embed_dim: dim,
|
||||
coherence_threshold: 0.9,
|
||||
coherence_hysteresis: 0.0,
|
||||
min_snr_db: 0.0,
|
||||
..FusionConfig::default()
|
||||
};
|
||||
let mut array = MultistaticArray::new(1, config);
|
||||
// Push incoherent phase diffs.
|
||||
for i in 0..100 {
|
||||
array.push_phase_diff(i as f32 * 0.5);
|
||||
}
|
||||
array.submit_viewpoint(make_viewpoint(0, 0, 4, dim)).unwrap();
|
||||
array.submit_viewpoint(make_viewpoint(1, 1, 4, dim)).unwrap();
|
||||
let result = array.fuse();
|
||||
assert!(matches!(result, Err(FusionError::CoherenceGateClosed { .. })));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_ungated_bypasses_coherence() {
|
||||
let dim = 16;
|
||||
let config = FusionConfig {
|
||||
embed_dim: dim,
|
||||
coherence_threshold: 0.99,
|
||||
coherence_hysteresis: 0.0,
|
||||
min_snr_db: 0.0,
|
||||
..FusionConfig::default()
|
||||
};
|
||||
let mut array = MultistaticArray::new(1, config);
|
||||
// Push incoherent diffs -- gate would be closed.
|
||||
for i in 0..100 {
|
||||
array.push_phase_diff(i as f32 * 0.5);
|
||||
}
|
||||
array.submit_viewpoint(make_viewpoint(0, 0, 4, dim)).unwrap();
|
||||
array.submit_viewpoint(make_viewpoint(1, 1, 4, dim)).unwrap();
|
||||
let fused = array.fuse_ungated().unwrap();
|
||||
assert_eq!(fused.embedding.len(), dim);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn submit_replaces_existing_viewpoint() {
|
||||
let dim = 8;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
let vp1 = make_viewpoint(10, 0, 4, dim);
|
||||
let mut vp2 = make_viewpoint(10, 1, 4, dim);
|
||||
vp2.snr_db = 25.0;
|
||||
array.submit_viewpoint(vp1).unwrap();
|
||||
assert_eq!(array.n_viewpoints(), 1);
|
||||
array.submit_viewpoint(vp2).unwrap();
|
||||
assert_eq!(array.n_viewpoints(), 1, "should replace, not add");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dimension_mismatch_returns_error() {
|
||||
let dim = 16;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
let mut vp = make_viewpoint(0, 0, 4, dim);
|
||||
vp.embedding = vec![1.0; 8]; // wrong dim
|
||||
assert!(matches!(
|
||||
array.submit_viewpoint(vp),
|
||||
Err(FusionError::DimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn snr_filter_rejects_low_quality() {
|
||||
let dim = 16;
|
||||
let config = FusionConfig {
|
||||
embed_dim: dim,
|
||||
coherence_threshold: 0.0,
|
||||
min_snr_db: 10.0,
|
||||
..FusionConfig::default()
|
||||
};
|
||||
let mut array = MultistaticArray::new(1, config);
|
||||
for _ in 0..60 {
|
||||
array.push_phase_diff(0.1);
|
||||
}
|
||||
let mut vp = make_viewpoint(0, 0, 4, dim);
|
||||
vp.snr_db = 3.0; // below threshold
|
||||
array.submit_viewpoint(vp).unwrap();
|
||||
assert!(matches!(array.fuse(), Err(FusionError::AllFiltered { .. })));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn events_are_emitted_on_fusion() {
|
||||
let dim = 8;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
array.submit_viewpoint(make_viewpoint(0, 0, 4, dim)).unwrap();
|
||||
array.submit_viewpoint(make_viewpoint(1, 1, 4, dim)).unwrap();
|
||||
array.clear_events();
|
||||
let _ = array.fuse();
|
||||
assert!(!array.events().is_empty(), "fusion should emit events");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn remove_viewpoint_works() {
|
||||
let dim = 8;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
array.submit_viewpoint(make_viewpoint(10, 0, 4, dim)).unwrap();
|
||||
array.submit_viewpoint(make_viewpoint(20, 1, 4, dim)).unwrap();
|
||||
assert_eq!(array.n_viewpoints(), 2);
|
||||
array.remove_viewpoint(10);
|
||||
assert_eq!(array.n_viewpoints(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fused_embedding_reports_gdi() {
|
||||
let dim = 16;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
for i in 0..4 {
|
||||
array.submit_viewpoint(make_viewpoint(i, i as usize, 4, dim)).unwrap();
|
||||
}
|
||||
let fused = array.fuse().unwrap();
|
||||
assert!(fused.gdi > 0.0, "GDI should be positive for spread viewpoints");
|
||||
assert!(fused.n_effective > 1.0, "effective viewpoints should be > 1");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn compute_gdi_standalone() {
|
||||
let dim = 8;
|
||||
let mut array = setup_coherent_array(dim);
|
||||
for i in 0..6 {
|
||||
array.submit_viewpoint(make_viewpoint(i, i as usize, 6, dim)).unwrap();
|
||||
}
|
||||
let gdi = array.compute_gdi().unwrap();
|
||||
assert!(gdi.value > 0.0);
|
||||
assert!(gdi.n_effective > 1.0);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,499 @@
|
||||
//! Geometric Diversity Index and Cramer-Rao bound estimation (ADR-031).
|
||||
//!
|
||||
//! Provides two key computations for array geometry quality assessment:
|
||||
//!
|
||||
//! 1. **Geometric Diversity Index (GDI)**: measures how well the viewpoints
|
||||
//! are spread around the sensing area. Higher GDI = better spatial coverage.
|
||||
//!
|
||||
//! 2. **Cramer-Rao Bound (CRB)**: lower bound on the position estimation
|
||||
//! variance achievable by any unbiased estimator given the array geometry.
|
||||
//! Used to predict theoretical localisation accuracy.
|
||||
//!
|
||||
//! Uses `ruvector_solver` for matrix operations in the Fisher information
|
||||
//! matrix inversion required by the Cramer-Rao bound.
|
||||
|
||||
use ruvector_solver::neumann::NeumannSolver;
|
||||
use ruvector_solver::types::CsrMatrix;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Node identifier
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Unique identifier for a sensor node in the multistatic array.
|
||||
pub type NodeId = u32;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// GeometricDiversityIndex
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Geometric Diversity Index measuring array viewpoint spread.
|
||||
///
|
||||
/// GDI is computed as the mean minimum angular separation across all viewpoints:
|
||||
///
|
||||
/// ```text
|
||||
/// GDI = (1/N) * sum_i min_{j != i} |theta_i - theta_j|
|
||||
/// ```
|
||||
///
|
||||
/// A GDI close to `2*PI/N` (uniform spacing) indicates optimal diversity.
|
||||
/// A GDI near zero means viewpoints are clustered.
|
||||
///
|
||||
/// The `n_effective` field estimates the number of independent viewpoints
|
||||
/// after accounting for angular correlation between nearby viewpoints.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GeometricDiversityIndex {
|
||||
/// GDI value (radians). Higher is better.
|
||||
pub value: f32,
|
||||
/// Effective independent viewpoints after correlation discount.
|
||||
pub n_effective: f32,
|
||||
/// Worst (most redundant) viewpoint pair.
|
||||
pub worst_pair: (NodeId, NodeId),
|
||||
/// Number of physical viewpoints in the array.
|
||||
pub n_physical: usize,
|
||||
}
|
||||
|
||||
impl GeometricDiversityIndex {
|
||||
/// Compute the GDI from viewpoint azimuth angles.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `azimuths`: per-viewpoint azimuth angle in radians from the array
|
||||
/// centroid. Must have at least 2 elements.
|
||||
/// - `node_ids`: per-viewpoint node identifier (same length as `azimuths`).
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `None` if fewer than 2 viewpoints are provided.
|
||||
pub fn compute(azimuths: &[f32], node_ids: &[NodeId]) -> Option<Self> {
|
||||
let n = azimuths.len();
|
||||
if n < 2 || node_ids.len() != n {
|
||||
return None;
|
||||
}
|
||||
|
||||
// Find the minimum angular separation for each viewpoint.
|
||||
let mut min_seps = Vec::with_capacity(n);
|
||||
let mut worst_sep = f32::MAX;
|
||||
let mut worst_i = 0_usize;
|
||||
let mut worst_j = 1_usize;
|
||||
|
||||
for i in 0..n {
|
||||
let mut min_sep = f32::MAX;
|
||||
let mut min_j = (i + 1) % n;
|
||||
for j in 0..n {
|
||||
if i == j {
|
||||
continue;
|
||||
}
|
||||
let sep = angular_distance(azimuths[i], azimuths[j]);
|
||||
if sep < min_sep {
|
||||
min_sep = sep;
|
||||
min_j = j;
|
||||
}
|
||||
}
|
||||
min_seps.push(min_sep);
|
||||
if min_sep < worst_sep {
|
||||
worst_sep = min_sep;
|
||||
worst_i = i;
|
||||
worst_j = min_j;
|
||||
}
|
||||
}
|
||||
|
||||
let gdi = min_seps.iter().sum::<f32>() / n as f32;
|
||||
|
||||
// Effective viewpoints: discount correlated viewpoints.
|
||||
// Correlation model: rho(theta) = exp(-theta^2 / (2 * sigma^2))
|
||||
// with sigma = PI/6 (30 degrees).
|
||||
let sigma = std::f32::consts::PI / 6.0;
|
||||
let n_effective = compute_effective_viewpoints(azimuths, sigma);
|
||||
|
||||
Some(GeometricDiversityIndex {
|
||||
value: gdi,
|
||||
n_effective,
|
||||
worst_pair: (node_ids[worst_i], node_ids[worst_j]),
|
||||
n_physical: n,
|
||||
})
|
||||
}
|
||||
|
||||
/// Returns `true` if the array has sufficient geometric diversity for
|
||||
/// reliable multi-viewpoint fusion.
|
||||
///
|
||||
/// Threshold: GDI >= PI / (2 * N) (at least half the uniform-spacing ideal).
|
||||
pub fn is_sufficient(&self) -> bool {
|
||||
if self.n_physical == 0 {
|
||||
return false;
|
||||
}
|
||||
let ideal = std::f32::consts::PI * 2.0 / self.n_physical as f32;
|
||||
self.value >= ideal * 0.5
|
||||
}
|
||||
|
||||
/// Ratio of effective to physical viewpoints.
|
||||
pub fn efficiency(&self) -> f32 {
|
||||
if self.n_physical == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
self.n_effective / self.n_physical as f32
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute the shortest angular distance between two angles (radians).
|
||||
///
|
||||
/// Returns a value in `[0, PI]`.
|
||||
fn angular_distance(a: f32, b: f32) -> f32 {
|
||||
let diff = (a - b).abs() % (2.0 * std::f32::consts::PI);
|
||||
if diff > std::f32::consts::PI {
|
||||
2.0 * std::f32::consts::PI - diff
|
||||
} else {
|
||||
diff
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute effective independent viewpoints using a Gaussian angular correlation
|
||||
/// model and eigenvalue analysis of the correlation matrix.
|
||||
///
|
||||
/// The effective count is: `N_eff = (sum lambda_i)^2 / sum(lambda_i^2)` where
|
||||
/// `lambda_i` are the eigenvalues of the angular correlation matrix. For
|
||||
/// efficiency, we approximate this using trace-based estimation:
|
||||
/// `N_eff approx trace(R)^2 / trace(R^2)`.
|
||||
fn compute_effective_viewpoints(azimuths: &[f32], sigma: f32) -> f32 {
|
||||
let n = azimuths.len();
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
if n == 1 {
|
||||
return 1.0;
|
||||
}
|
||||
|
||||
let two_sigma_sq = 2.0 * sigma * sigma;
|
||||
|
||||
// Build correlation matrix R[i,j] = exp(-angular_dist(i,j)^2 / (2*sigma^2))
|
||||
// and compute trace(R) and trace(R^2) simultaneously.
|
||||
// For trace(R^2) = sum_i sum_j R[i,j]^2, we need the full matrix.
|
||||
let mut r_matrix = vec![0.0_f32; n * n];
|
||||
for i in 0..n {
|
||||
r_matrix[i * n + i] = 1.0;
|
||||
for j in (i + 1)..n {
|
||||
let d = angular_distance(azimuths[i], azimuths[j]);
|
||||
let rho = (-d * d / two_sigma_sq).exp();
|
||||
r_matrix[i * n + j] = rho;
|
||||
r_matrix[j * n + i] = rho;
|
||||
}
|
||||
}
|
||||
|
||||
// trace(R) = n (all diagonal entries are 1.0).
|
||||
let trace_r = n as f32;
|
||||
// trace(R^2) = sum_{i,j} R[i,j]^2
|
||||
let trace_r2: f32 = r_matrix.iter().map(|v| v * v).sum();
|
||||
|
||||
// N_eff = trace(R)^2 / trace(R^2)
|
||||
let n_eff = (trace_r * trace_r) / trace_r2.max(f32::EPSILON);
|
||||
n_eff.min(n as f32).max(1.0)
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Cramer-Rao Bound
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Cramer-Rao lower bound on position estimation variance.
|
||||
///
|
||||
/// The CRB provides the theoretical minimum variance achievable by any
|
||||
/// unbiased estimator for the target position given the array geometry.
|
||||
/// Lower CRB = better localisation potential.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CramerRaoBound {
|
||||
/// CRB for x-coordinate estimation (metres squared).
|
||||
pub crb_x: f32,
|
||||
/// CRB for y-coordinate estimation (metres squared).
|
||||
pub crb_y: f32,
|
||||
/// Root-mean-square position error lower bound (metres).
|
||||
pub rmse_lower_bound: f32,
|
||||
/// Geometric dilution of precision (GDOP).
|
||||
pub gdop: f32,
|
||||
}
|
||||
|
||||
/// A viewpoint position for CRB computation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ViewpointPosition {
|
||||
/// X coordinate in metres.
|
||||
pub x: f32,
|
||||
/// Y coordinate in metres.
|
||||
pub y: f32,
|
||||
/// Per-measurement noise standard deviation (metres).
|
||||
pub noise_std: f32,
|
||||
}
|
||||
|
||||
impl CramerRaoBound {
|
||||
/// Estimate the Cramer-Rao bound for a target at `(tx, ty)` observed by
|
||||
/// the given viewpoints.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `target`: target position `(x, y)` in metres.
|
||||
/// - `viewpoints`: sensor node positions with per-node noise levels.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `None` if fewer than 3 viewpoints are provided (under-determined).
|
||||
pub fn estimate(target: (f32, f32), viewpoints: &[ViewpointPosition]) -> Option<Self> {
|
||||
let n = viewpoints.len();
|
||||
if n < 3 {
|
||||
return None;
|
||||
}
|
||||
|
||||
// Build the 2x2 Fisher Information Matrix (FIM).
|
||||
// FIM = sum_i (1/sigma_i^2) * [cos^2(phi_i), cos(phi_i)*sin(phi_i);
|
||||
// cos(phi_i)*sin(phi_i), sin^2(phi_i)]
|
||||
// where phi_i is the bearing angle from viewpoint i to the target.
|
||||
let mut fim_00 = 0.0_f32;
|
||||
let mut fim_01 = 0.0_f32;
|
||||
let mut fim_11 = 0.0_f32;
|
||||
|
||||
for vp in viewpoints {
|
||||
let dx = target.0 - vp.x;
|
||||
let dy = target.1 - vp.y;
|
||||
let r = (dx * dx + dy * dy).sqrt().max(1e-6);
|
||||
let cos_phi = dx / r;
|
||||
let sin_phi = dy / r;
|
||||
let inv_var = 1.0 / (vp.noise_std * vp.noise_std).max(1e-10);
|
||||
|
||||
fim_00 += inv_var * cos_phi * cos_phi;
|
||||
fim_01 += inv_var * cos_phi * sin_phi;
|
||||
fim_11 += inv_var * sin_phi * sin_phi;
|
||||
}
|
||||
|
||||
// Invert the 2x2 FIM analytically: CRB = FIM^{-1}.
|
||||
let det = fim_00 * fim_11 - fim_01 * fim_01;
|
||||
if det.abs() < 1e-12 {
|
||||
return None;
|
||||
}
|
||||
|
||||
let crb_x = fim_11 / det;
|
||||
let crb_y = fim_00 / det;
|
||||
let rmse = (crb_x + crb_y).sqrt();
|
||||
let gdop = (crb_x + crb_y).sqrt();
|
||||
|
||||
Some(CramerRaoBound {
|
||||
crb_x,
|
||||
crb_y,
|
||||
rmse_lower_bound: rmse,
|
||||
gdop,
|
||||
})
|
||||
}
|
||||
|
||||
/// Compute the CRB using the `ruvector-solver` Neumann series solver for
|
||||
/// larger arrays where the analytic 2x2 inversion is extended to include
|
||||
/// regularisation for ill-conditioned geometries.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `target`: target position `(x, y)` in metres.
|
||||
/// - `viewpoints`: sensor node positions with per-node noise levels.
|
||||
/// - `regularisation`: Tikhonov regularisation parameter (typically 1e-4).
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `None` if fewer than 3 viewpoints or the solver fails.
|
||||
pub fn estimate_regularised(
|
||||
target: (f32, f32),
|
||||
viewpoints: &[ViewpointPosition],
|
||||
regularisation: f32,
|
||||
) -> Option<Self> {
|
||||
let n = viewpoints.len();
|
||||
if n < 3 {
|
||||
return None;
|
||||
}
|
||||
|
||||
let mut fim_00 = regularisation;
|
||||
let mut fim_01 = 0.0_f32;
|
||||
let mut fim_11 = regularisation;
|
||||
|
||||
for vp in viewpoints {
|
||||
let dx = target.0 - vp.x;
|
||||
let dy = target.1 - vp.y;
|
||||
let r = (dx * dx + dy * dy).sqrt().max(1e-6);
|
||||
let cos_phi = dx / r;
|
||||
let sin_phi = dy / r;
|
||||
let inv_var = 1.0 / (vp.noise_std * vp.noise_std).max(1e-10);
|
||||
|
||||
fim_00 += inv_var * cos_phi * cos_phi;
|
||||
fim_01 += inv_var * cos_phi * sin_phi;
|
||||
fim_11 += inv_var * sin_phi * sin_phi;
|
||||
}
|
||||
|
||||
// Use Neumann solver for the regularised system.
|
||||
let ata = CsrMatrix::<f32>::from_coo(
|
||||
2,
|
||||
2,
|
||||
vec![
|
||||
(0, 0, fim_00),
|
||||
(0, 1, fim_01),
|
||||
(1, 0, fim_01),
|
||||
(1, 1, fim_11),
|
||||
],
|
||||
);
|
||||
|
||||
// Solve FIM * x = e_1 and FIM * x = e_2 to get the CRB diagonal.
|
||||
let solver = NeumannSolver::new(1e-6, 500);
|
||||
|
||||
let crb_x = solver
|
||||
.solve(&ata, &[1.0, 0.0])
|
||||
.ok()
|
||||
.map(|r| r.solution[0])?;
|
||||
let crb_y = solver
|
||||
.solve(&ata, &[0.0, 1.0])
|
||||
.ok()
|
||||
.map(|r| r.solution[1])?;
|
||||
|
||||
let rmse = (crb_x.abs() + crb_y.abs()).sqrt();
|
||||
|
||||
Some(CramerRaoBound {
|
||||
crb_x,
|
||||
crb_y,
|
||||
rmse_lower_bound: rmse,
|
||||
gdop: rmse,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn gdi_uniform_spacing_is_optimal() {
|
||||
// 4 viewpoints at 0, 90, 180, 270 degrees
|
||||
let azimuths = vec![0.0, std::f32::consts::FRAC_PI_2, std::f32::consts::PI, 3.0 * std::f32::consts::FRAC_PI_2];
|
||||
let ids = vec![0, 1, 2, 3];
|
||||
let gdi = GeometricDiversityIndex::compute(&azimuths, &ids).unwrap();
|
||||
// Minimum separation = PI/2 for each viewpoint, so GDI = PI/2
|
||||
let expected = std::f32::consts::FRAC_PI_2;
|
||||
assert!(
|
||||
(gdi.value - expected).abs() < 0.01,
|
||||
"uniform spacing GDI should be PI/2={expected:.3}, got {:.3}",
|
||||
gdi.value
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gdi_clustered_viewpoints_have_low_value() {
|
||||
// 4 viewpoints clustered within 10 degrees
|
||||
let azimuths = vec![0.0, 0.05, 0.08, 0.12];
|
||||
let ids = vec![0, 1, 2, 3];
|
||||
let gdi = GeometricDiversityIndex::compute(&azimuths, &ids).unwrap();
|
||||
assert!(
|
||||
gdi.value < 0.15,
|
||||
"clustered viewpoints should have low GDI, got {:.3}",
|
||||
gdi.value
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gdi_insufficient_viewpoints_returns_none() {
|
||||
assert!(GeometricDiversityIndex::compute(&[0.0], &[0]).is_none());
|
||||
assert!(GeometricDiversityIndex::compute(&[], &[]).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gdi_efficiency_is_bounded() {
|
||||
let azimuths = vec![0.0, 1.0, 2.0, 3.0];
|
||||
let ids = vec![0, 1, 2, 3];
|
||||
let gdi = GeometricDiversityIndex::compute(&azimuths, &ids).unwrap();
|
||||
assert!(gdi.efficiency() > 0.0 && gdi.efficiency() <= 1.0,
|
||||
"efficiency should be in (0, 1], got {}", gdi.efficiency());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gdi_is_sufficient_for_uniform_layout() {
|
||||
let azimuths = vec![0.0, std::f32::consts::FRAC_PI_2, std::f32::consts::PI, 3.0 * std::f32::consts::FRAC_PI_2];
|
||||
let ids = vec![0, 1, 2, 3];
|
||||
let gdi = GeometricDiversityIndex::compute(&azimuths, &ids).unwrap();
|
||||
assert!(gdi.is_sufficient(), "uniform layout should be sufficient");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gdi_worst_pair_is_closest() {
|
||||
// Viewpoints at 0, 0.1, PI, 1.5*PI
|
||||
let azimuths = vec![0.0, 0.1, std::f32::consts::PI, 1.5 * std::f32::consts::PI];
|
||||
let ids = vec![10, 20, 30, 40];
|
||||
let gdi = GeometricDiversityIndex::compute(&azimuths, &ids).unwrap();
|
||||
// Worst pair should be (10, 20) as they are only 0.1 rad apart
|
||||
assert!(
|
||||
(gdi.worst_pair == (10, 20)) || (gdi.worst_pair == (20, 10)),
|
||||
"worst pair should be nodes 10 and 20, got {:?}",
|
||||
gdi.worst_pair
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn angular_distance_wraps_correctly() {
|
||||
let d = angular_distance(0.1, 2.0 * std::f32::consts::PI - 0.1);
|
||||
assert!(
|
||||
(d - 0.2).abs() < 1e-4,
|
||||
"angular distance across 0/2PI boundary should be 0.2, got {d}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn effective_viewpoints_all_identical_equals_one() {
|
||||
let azimuths = vec![0.0, 0.0, 0.0, 0.0];
|
||||
let sigma = std::f32::consts::PI / 6.0;
|
||||
let n_eff = compute_effective_viewpoints(&azimuths, sigma);
|
||||
assert!(
|
||||
(n_eff - 1.0).abs() < 0.1,
|
||||
"4 identical viewpoints should have n_eff ~ 1.0, got {n_eff}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn crb_decreases_with_more_viewpoints() {
|
||||
let target = (0.0, 0.0);
|
||||
let vp3: Vec<ViewpointPosition> = (0..3)
|
||||
.map(|i| {
|
||||
let a = 2.0 * std::f32::consts::PI * i as f32 / 3.0;
|
||||
ViewpointPosition { x: 5.0 * a.cos(), y: 5.0 * a.sin(), noise_std: 0.1 }
|
||||
})
|
||||
.collect();
|
||||
let vp6: Vec<ViewpointPosition> = (0..6)
|
||||
.map(|i| {
|
||||
let a = 2.0 * std::f32::consts::PI * i as f32 / 6.0;
|
||||
ViewpointPosition { x: 5.0 * a.cos(), y: 5.0 * a.sin(), noise_std: 0.1 }
|
||||
})
|
||||
.collect();
|
||||
|
||||
let crb3 = CramerRaoBound::estimate(target, &vp3).unwrap();
|
||||
let crb6 = CramerRaoBound::estimate(target, &vp6).unwrap();
|
||||
assert!(
|
||||
crb6.rmse_lower_bound < crb3.rmse_lower_bound,
|
||||
"6 viewpoints should give lower CRB than 3: {:.4} vs {:.4}",
|
||||
crb6.rmse_lower_bound,
|
||||
crb3.rmse_lower_bound
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn crb_too_few_viewpoints_returns_none() {
|
||||
let target = (0.0, 0.0);
|
||||
let vps = vec![
|
||||
ViewpointPosition { x: 1.0, y: 0.0, noise_std: 0.1 },
|
||||
ViewpointPosition { x: 0.0, y: 1.0, noise_std: 0.1 },
|
||||
];
|
||||
assert!(CramerRaoBound::estimate(target, &vps).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn crb_regularised_returns_result() {
|
||||
let target = (0.0, 0.0);
|
||||
let vps: Vec<ViewpointPosition> = (0..4)
|
||||
.map(|i| {
|
||||
let a = 2.0 * std::f32::consts::PI * i as f32 / 4.0;
|
||||
ViewpointPosition { x: 3.0 * a.cos(), y: 3.0 * a.sin(), noise_std: 0.1 }
|
||||
})
|
||||
.collect();
|
||||
let crb = CramerRaoBound::estimate_regularised(target, &vps, 1e-4);
|
||||
// May return None if Neumann solver doesn't converge, but should not panic.
|
||||
if let Some(crb) = crb {
|
||||
assert!(crb.rmse_lower_bound >= 0.0, "RMSE bound must be non-negative");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
//! Cross-viewpoint embedding fusion for multistatic WiFi sensing (ADR-031).
|
||||
//!
|
||||
//! This module implements the RuView fusion pipeline that combines per-viewpoint
|
||||
//! AETHER embeddings into a single fused embedding using learned cross-viewpoint
|
||||
//! attention with geometric bias.
|
||||
//!
|
||||
//! # Submodules
|
||||
//!
|
||||
//! - [`attention`]: Cross-viewpoint scaled dot-product attention with geometric
|
||||
//! bias encoding angular separation and baseline distance between viewpoint pairs.
|
||||
//! - [`geometry`]: Geometric Diversity Index (GDI) computation and Cramer-Rao
|
||||
//! bound estimation for array geometry quality assessment.
|
||||
//! - [`coherence`]: Coherence gating that determines whether the environment is
|
||||
//! stable enough for a model update based on phase consistency.
|
||||
//! - [`fusion`]: `MultistaticArray` aggregate root that orchestrates the full
|
||||
//! fusion pipeline from per-viewpoint embeddings to a single fused output.
|
||||
|
||||
pub mod attention;
|
||||
pub mod coherence;
|
||||
pub mod fusion;
|
||||
pub mod geometry;
|
||||
|
||||
// Re-export primary types at the module root for ergonomic imports.
|
||||
pub use attention::{CrossViewpointAttention, GeometricBias};
|
||||
pub use coherence::{CoherenceGate, CoherenceState};
|
||||
pub use fusion::{FusedEmbedding, FusionConfig, MultistaticArray, ViewpointEmbedding};
|
||||
pub use geometry::{CramerRaoBound, GeometricDiversityIndex};
|
||||
@@ -40,6 +40,7 @@ pub mod hampel;
|
||||
pub mod hardware_norm;
|
||||
pub mod motion;
|
||||
pub mod phase_sanitizer;
|
||||
pub mod ruvsense;
|
||||
pub mod spectrogram;
|
||||
pub mod subcarrier_selection;
|
||||
|
||||
|
||||
@@ -0,0 +1,586 @@
|
||||
//! Adversarial detection: physically impossible signal identification.
|
||||
//!
|
||||
//! Detects spoofed or injected WiFi signals by checking multi-link
|
||||
//! consistency, field model constraint violations, and physical
|
||||
//! plausibility. A single-link injection cannot fool a multistatic
|
||||
//! mesh because it would violate geometric constraints across links.
|
||||
//!
|
||||
//! # Checks
|
||||
//! 1. **Multi-link consistency**: A real body perturbs all links that
|
||||
//! traverse its location. An injection affects only the targeted link.
|
||||
//! 2. **Field model constraints**: Perturbation must be consistent with
|
||||
//! the room's eigenmode structure.
|
||||
//! 3. **Temporal continuity**: Real movement is smooth; injections cause
|
||||
//! discontinuities in embedding space.
|
||||
//! 4. **Energy conservation**: Total perturbation energy across links
|
||||
//! must be consistent with the number and size of bodies present.
|
||||
//!
|
||||
//! # References
|
||||
//! - ADR-030 Tier 7: Adversarial Detection
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from adversarial detection.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum AdversarialError {
|
||||
/// Insufficient links for multi-link consistency check.
|
||||
#[error("Insufficient links: need >= {needed}, got {got}")]
|
||||
InsufficientLinks { needed: usize, got: usize },
|
||||
|
||||
/// Dimension mismatch.
|
||||
#[error("Dimension mismatch: expected {expected}, got {got}")]
|
||||
DimensionMismatch { expected: usize, got: usize },
|
||||
|
||||
/// No baseline available for constraint checking.
|
||||
#[error("No baseline available — calibrate field model first")]
|
||||
NoBaseline,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Configuration for adversarial detection.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct AdversarialConfig {
|
||||
/// Number of links in the mesh.
|
||||
pub n_links: usize,
|
||||
/// Minimum links for multi-link consistency (default 4).
|
||||
pub min_links: usize,
|
||||
/// Consistency threshold: fraction of links that must agree (0.0-1.0).
|
||||
pub consistency_threshold: f64,
|
||||
/// Maximum allowed energy ratio between any single link and total.
|
||||
pub max_single_link_energy_ratio: f64,
|
||||
/// Maximum allowed temporal discontinuity in embedding space.
|
||||
pub max_temporal_discontinuity: f64,
|
||||
/// Maximum allowed perturbation energy per body.
|
||||
pub max_energy_per_body: f64,
|
||||
}
|
||||
|
||||
impl Default for AdversarialConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
n_links: 12,
|
||||
min_links: 4,
|
||||
consistency_threshold: 0.6,
|
||||
max_single_link_energy_ratio: 0.5,
|
||||
max_temporal_discontinuity: 5.0,
|
||||
max_energy_per_body: 100.0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Detection results
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Type of adversarial anomaly detected.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum AnomalyType {
|
||||
/// Single link shows perturbation inconsistent with other links.
|
||||
SingleLinkInjection,
|
||||
/// Perturbation violates field model eigenmode structure.
|
||||
FieldModelViolation,
|
||||
/// Sudden discontinuity in embedding trajectory.
|
||||
TemporalDiscontinuity,
|
||||
/// Total perturbation energy inconsistent with occupancy.
|
||||
EnergyViolation,
|
||||
/// Multiple anomaly types detected simultaneously.
|
||||
MultipleViolations,
|
||||
}
|
||||
|
||||
impl AnomalyType {
|
||||
/// Human-readable name.
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
AnomalyType::SingleLinkInjection => "single_link_injection",
|
||||
AnomalyType::FieldModelViolation => "field_model_violation",
|
||||
AnomalyType::TemporalDiscontinuity => "temporal_discontinuity",
|
||||
AnomalyType::EnergyViolation => "energy_violation",
|
||||
AnomalyType::MultipleViolations => "multiple_violations",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Result of adversarial detection on one frame.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct AdversarialResult {
|
||||
/// Whether any anomaly was detected.
|
||||
pub anomaly_detected: bool,
|
||||
/// Type of anomaly (if detected).
|
||||
pub anomaly_type: Option<AnomalyType>,
|
||||
/// Anomaly score (0.0 = clean, 1.0 = definitely adversarial).
|
||||
pub anomaly_score: f64,
|
||||
/// Per-check results.
|
||||
pub checks: CheckResults,
|
||||
/// Affected link indices (if single-link injection).
|
||||
pub affected_links: Vec<usize>,
|
||||
/// Timestamp (microseconds).
|
||||
pub timestamp_us: u64,
|
||||
}
|
||||
|
||||
/// Results of individual checks.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CheckResults {
|
||||
/// Multi-link consistency score (0.0 = inconsistent, 1.0 = fully consistent).
|
||||
pub consistency_score: f64,
|
||||
/// Field model residual score (lower = more consistent with modes).
|
||||
pub field_model_residual: f64,
|
||||
/// Temporal continuity score (lower = smoother).
|
||||
pub temporal_continuity: f64,
|
||||
/// Energy conservation score (closer to 1.0 = consistent).
|
||||
pub energy_ratio: f64,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Adversarial detector
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Adversarial signal detector for the multistatic mesh.
|
||||
///
|
||||
/// Checks each frame for physical plausibility across multiple
|
||||
/// independent criteria. A spoofed signal that passes one check
|
||||
/// is unlikely to pass all of them.
|
||||
#[derive(Debug)]
|
||||
pub struct AdversarialDetector {
|
||||
config: AdversarialConfig,
|
||||
/// Previous frame's per-link energies (for temporal continuity).
|
||||
prev_energies: Option<Vec<f64>>,
|
||||
/// Previous frame's total energy.
|
||||
prev_total_energy: Option<f64>,
|
||||
/// Total frames processed.
|
||||
total_frames: u64,
|
||||
/// Total anomalies detected.
|
||||
anomaly_count: u64,
|
||||
}
|
||||
|
||||
impl AdversarialDetector {
|
||||
/// Create a new adversarial detector.
|
||||
pub fn new(config: AdversarialConfig) -> Result<Self, AdversarialError> {
|
||||
if config.n_links < config.min_links {
|
||||
return Err(AdversarialError::InsufficientLinks {
|
||||
needed: config.min_links,
|
||||
got: config.n_links,
|
||||
});
|
||||
}
|
||||
Ok(Self {
|
||||
config,
|
||||
prev_energies: None,
|
||||
prev_total_energy: None,
|
||||
total_frames: 0,
|
||||
anomaly_count: 0,
|
||||
})
|
||||
}
|
||||
|
||||
/// Check a frame for adversarial anomalies.
|
||||
///
|
||||
/// `link_energies`: per-link perturbation energy (from field model).
|
||||
/// `n_bodies`: estimated number of bodies present.
|
||||
/// `timestamp_us`: frame timestamp.
|
||||
pub fn check(
|
||||
&mut self,
|
||||
link_energies: &[f64],
|
||||
n_bodies: usize,
|
||||
timestamp_us: u64,
|
||||
) -> Result<AdversarialResult, AdversarialError> {
|
||||
if link_energies.len() != self.config.n_links {
|
||||
return Err(AdversarialError::DimensionMismatch {
|
||||
expected: self.config.n_links,
|
||||
got: link_energies.len(),
|
||||
});
|
||||
}
|
||||
|
||||
self.total_frames += 1;
|
||||
|
||||
let total_energy: f64 = link_energies.iter().sum();
|
||||
|
||||
// Check 1: Multi-link consistency
|
||||
let consistency = self.check_consistency(link_energies, total_energy);
|
||||
|
||||
// Check 2: Field model residual (simplified — check energy distribution)
|
||||
let field_residual = self.check_field_model(link_energies, total_energy);
|
||||
|
||||
// Check 3: Temporal continuity
|
||||
let temporal = self.check_temporal(link_energies, total_energy);
|
||||
|
||||
// Check 4: Energy conservation
|
||||
let energy_ratio = self.check_energy(total_energy, n_bodies);
|
||||
|
||||
// Store for next frame
|
||||
self.prev_energies = Some(link_energies.to_vec());
|
||||
self.prev_total_energy = Some(total_energy);
|
||||
|
||||
let checks = CheckResults {
|
||||
consistency_score: consistency,
|
||||
field_model_residual: field_residual,
|
||||
temporal_continuity: temporal,
|
||||
energy_ratio,
|
||||
};
|
||||
|
||||
// Aggregate anomaly score
|
||||
let mut violations = Vec::new();
|
||||
|
||||
if consistency < self.config.consistency_threshold {
|
||||
violations.push(AnomalyType::SingleLinkInjection);
|
||||
}
|
||||
if field_residual > 0.8 {
|
||||
violations.push(AnomalyType::FieldModelViolation);
|
||||
}
|
||||
if temporal > self.config.max_temporal_discontinuity {
|
||||
violations.push(AnomalyType::TemporalDiscontinuity);
|
||||
}
|
||||
if energy_ratio > 2.0 || (n_bodies > 0 && energy_ratio < 0.1) {
|
||||
violations.push(AnomalyType::EnergyViolation);
|
||||
}
|
||||
|
||||
let anomaly_detected = !violations.is_empty();
|
||||
let anomaly_type = match violations.len() {
|
||||
0 => None,
|
||||
1 => Some(violations[0]),
|
||||
_ => Some(AnomalyType::MultipleViolations),
|
||||
};
|
||||
|
||||
// Score: weighted combination
|
||||
let anomaly_score = ((1.0 - consistency) * 0.4
|
||||
+ field_residual * 0.2
|
||||
+ (temporal / self.config.max_temporal_discontinuity).min(1.0) * 0.2
|
||||
+ ((energy_ratio - 1.0).abs() / 2.0).min(1.0) * 0.2)
|
||||
.clamp(0.0, 1.0);
|
||||
|
||||
// Find affected links (highest single-link energy ratio)
|
||||
let affected_links = if anomaly_detected {
|
||||
self.find_anomalous_links(link_energies, total_energy)
|
||||
} else {
|
||||
Vec::new()
|
||||
};
|
||||
|
||||
if anomaly_detected {
|
||||
self.anomaly_count += 1;
|
||||
}
|
||||
|
||||
Ok(AdversarialResult {
|
||||
anomaly_detected,
|
||||
anomaly_type,
|
||||
anomaly_score,
|
||||
checks,
|
||||
affected_links,
|
||||
timestamp_us,
|
||||
})
|
||||
}
|
||||
|
||||
/// Multi-link consistency: what fraction of links have correlated energy?
|
||||
///
|
||||
/// A real body perturbs many links. An injection affects few.
|
||||
fn check_consistency(&self, energies: &[f64], total: f64) -> f64 {
|
||||
if total < 1e-15 {
|
||||
return 1.0; // No perturbation = consistent (empty room)
|
||||
}
|
||||
|
||||
let mean = total / energies.len() as f64;
|
||||
let threshold = mean * 0.1; // link must have at least 10% of mean energy
|
||||
|
||||
let active_count = energies.iter().filter(|&&e| e > threshold).count();
|
||||
active_count as f64 / energies.len() as f64
|
||||
}
|
||||
|
||||
/// Field model check: is energy distribution consistent with physical propagation?
|
||||
///
|
||||
/// In a real scenario, energy should be distributed across links
|
||||
/// based on geometry. A concentrated injection scores high residual.
|
||||
fn check_field_model(&self, energies: &[f64], total: f64) -> f64 {
|
||||
if total < 1e-15 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Compute Gini coefficient of energy distribution
|
||||
// Gini = 0 → perfectly uniform, Gini = 1 → all in one link
|
||||
let n = energies.len() as f64;
|
||||
let mut sorted: Vec<f64> = energies.to_vec();
|
||||
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||||
|
||||
let numerator: f64 = sorted
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, &x)| (2.0 * (i + 1) as f64 - n - 1.0) * x)
|
||||
.sum();
|
||||
|
||||
let gini = numerator / (n * total);
|
||||
gini.clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// Temporal continuity: how much did per-link energies change from previous frame?
|
||||
fn check_temporal(&self, energies: &[f64], _total: f64) -> f64 {
|
||||
match &self.prev_energies {
|
||||
None => 0.0, // First frame, no temporal check
|
||||
Some(prev) => {
|
||||
let diff_energy: f64 = energies
|
||||
.iter()
|
||||
.zip(prev.iter())
|
||||
.map(|(&a, &b)| (a - b) * (a - b))
|
||||
.sum::<f64>()
|
||||
.sqrt();
|
||||
diff_energy
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Energy conservation: is total energy consistent with body count?
|
||||
fn check_energy(&self, total_energy: f64, n_bodies: usize) -> f64 {
|
||||
if n_bodies == 0 {
|
||||
// No bodies: any energy is suspicious
|
||||
return if total_energy > 1e-10 {
|
||||
total_energy
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
}
|
||||
let expected = n_bodies as f64 * self.config.max_energy_per_body;
|
||||
if expected < 1e-15 {
|
||||
return 0.0;
|
||||
}
|
||||
total_energy / expected
|
||||
}
|
||||
|
||||
/// Find links that are anomalously high relative to the mean.
|
||||
fn find_anomalous_links(&self, energies: &[f64], total: f64) -> Vec<usize> {
|
||||
if total < 1e-15 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
energies
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, &e)| e / total > self.config.max_single_link_energy_ratio)
|
||||
.map(|(i, _)| i)
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Total frames processed.
|
||||
pub fn total_frames(&self) -> u64 {
|
||||
self.total_frames
|
||||
}
|
||||
|
||||
/// Total anomalies detected.
|
||||
pub fn anomaly_count(&self) -> u64 {
|
||||
self.anomaly_count
|
||||
}
|
||||
|
||||
/// Anomaly rate (anomalies / total frames).
|
||||
pub fn anomaly_rate(&self) -> f64 {
|
||||
if self.total_frames == 0 {
|
||||
0.0
|
||||
} else {
|
||||
self.anomaly_count as f64 / self.total_frames as f64
|
||||
}
|
||||
}
|
||||
|
||||
/// Reset detector state.
|
||||
pub fn reset(&mut self) {
|
||||
self.prev_energies = None;
|
||||
self.prev_total_energy = None;
|
||||
self.total_frames = 0;
|
||||
self.anomaly_count = 0;
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn default_config() -> AdversarialConfig {
|
||||
AdversarialConfig {
|
||||
n_links: 6,
|
||||
min_links: 4,
|
||||
consistency_threshold: 0.6,
|
||||
max_single_link_energy_ratio: 0.5,
|
||||
max_temporal_discontinuity: 5.0,
|
||||
max_energy_per_body: 10.0,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_detector_creation() {
|
||||
let det = AdversarialDetector::new(default_config()).unwrap();
|
||||
assert_eq!(det.total_frames(), 0);
|
||||
assert_eq!(det.anomaly_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_insufficient_links() {
|
||||
let config = AdversarialConfig {
|
||||
n_links: 2,
|
||||
min_links: 4,
|
||||
..default_config()
|
||||
};
|
||||
assert!(matches!(
|
||||
AdversarialDetector::new(config),
|
||||
Err(AdversarialError::InsufficientLinks { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_clean_frame_no_anomaly() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
|
||||
// Uniform energy across all links (real body)
|
||||
let energies = vec![1.0, 1.1, 0.9, 1.0, 1.05, 0.95];
|
||||
let result = det.check(&energies, 1, 0).unwrap();
|
||||
|
||||
assert!(
|
||||
!result.anomaly_detected,
|
||||
"Uniform energy should not trigger anomaly"
|
||||
);
|
||||
assert!(result.anomaly_score < 0.5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_single_link_injection_detected() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
|
||||
// All energy on one link (injection)
|
||||
let energies = vec![10.0, 0.0, 0.0, 0.0, 0.0, 0.0];
|
||||
let result = det.check(&energies, 0, 0).unwrap();
|
||||
|
||||
assert!(
|
||||
result.anomaly_detected,
|
||||
"Single-link injection should be detected"
|
||||
);
|
||||
assert!(result.affected_links.contains(&0));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty_room_no_anomaly() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
|
||||
let energies = vec![0.0; 6];
|
||||
let result = det.check(&energies, 0, 0).unwrap();
|
||||
|
||||
assert!(!result.anomaly_detected);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_temporal_discontinuity() {
|
||||
let mut det = AdversarialDetector::new(AdversarialConfig {
|
||||
max_temporal_discontinuity: 1.0, // strict
|
||||
..default_config()
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
// Frame 1: low energy
|
||||
let energies1 = vec![0.1; 6];
|
||||
det.check(&energies1, 0, 0).unwrap();
|
||||
|
||||
// Frame 2: sudden massive energy (discontinuity)
|
||||
let energies2 = vec![100.0; 6];
|
||||
let result = det.check(&energies2, 0, 50_000).unwrap();
|
||||
|
||||
assert!(
|
||||
result.anomaly_detected,
|
||||
"Temporal discontinuity should be detected"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_energy_violation_too_high() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
|
||||
// Way more energy than 1 body should produce
|
||||
let energies = vec![100.0; 6]; // total = 600, max_per_body = 10
|
||||
let result = det.check(&energies, 1, 0).unwrap();
|
||||
|
||||
assert!(
|
||||
result.anomaly_detected,
|
||||
"Excessive energy should trigger anomaly"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_dimension_mismatch() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
let result = det.check(&[1.0, 2.0], 0, 0);
|
||||
assert!(matches!(
|
||||
result,
|
||||
Err(AdversarialError::DimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_anomaly_rate() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
|
||||
// 2 clean frames
|
||||
det.check(&vec![1.0; 6], 1, 0).unwrap();
|
||||
det.check(&vec![1.0; 6], 1, 50_000).unwrap();
|
||||
|
||||
// 1 anomalous frame
|
||||
det.check(&vec![10.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0, 100_000)
|
||||
.unwrap();
|
||||
|
||||
assert_eq!(det.total_frames(), 3);
|
||||
assert!(det.anomaly_count() >= 1);
|
||||
assert!(det.anomaly_rate() > 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_reset() {
|
||||
let mut det = AdversarialDetector::new(default_config()).unwrap();
|
||||
det.check(&vec![1.0; 6], 1, 0).unwrap();
|
||||
det.reset();
|
||||
|
||||
assert_eq!(det.total_frames(), 0);
|
||||
assert_eq!(det.anomaly_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_anomaly_type_names() {
|
||||
assert_eq!(
|
||||
AnomalyType::SingleLinkInjection.name(),
|
||||
"single_link_injection"
|
||||
);
|
||||
assert_eq!(
|
||||
AnomalyType::FieldModelViolation.name(),
|
||||
"field_model_violation"
|
||||
);
|
||||
assert_eq!(
|
||||
AnomalyType::TemporalDiscontinuity.name(),
|
||||
"temporal_discontinuity"
|
||||
);
|
||||
assert_eq!(AnomalyType::EnergyViolation.name(), "energy_violation");
|
||||
assert_eq!(
|
||||
AnomalyType::MultipleViolations.name(),
|
||||
"multiple_violations"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_gini_coefficient_uniform() {
|
||||
let det = AdversarialDetector::new(default_config()).unwrap();
|
||||
let energies = vec![1.0; 6];
|
||||
let total = 6.0;
|
||||
let gini = det.check_field_model(&energies, total);
|
||||
assert!(
|
||||
gini < 0.1,
|
||||
"Uniform distribution should have low Gini: {}",
|
||||
gini
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_gini_coefficient_concentrated() {
|
||||
let det = AdversarialDetector::new(default_config()).unwrap();
|
||||
let energies = vec![6.0, 0.0, 0.0, 0.0, 0.0, 0.0];
|
||||
let total = 6.0;
|
||||
let gini = det.check_field_model(&energies, total);
|
||||
assert!(
|
||||
gini > 0.5,
|
||||
"Concentrated distribution should have high Gini: {}",
|
||||
gini
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,464 @@
|
||||
//! Coherence Metric Computation (ADR-029 Section 2.5)
|
||||
//!
|
||||
//! Per-link coherence quantifies consistency of the current CSI observation
|
||||
//! with a running reference template. The metric is computed as a weighted
|
||||
//! mean of per-subcarrier Gaussian likelihoods:
|
||||
//!
|
||||
//! score = sum(w_i * exp(-0.5 * z_i^2)) / sum(w_i)
|
||||
//!
|
||||
//! where z_i = |current_i - reference_i| / sqrt(variance_i) and
|
||||
//! w_i = 1 / (variance_i + epsilon).
|
||||
//!
|
||||
//! Low-variance (stable) subcarriers dominate the score, making it
|
||||
//! sensitive to environmental drift while tolerant of body-motion
|
||||
//! subcarrier fluctuations.
|
||||
//!
|
||||
//! # RuVector Integration
|
||||
//!
|
||||
//! Uses `ruvector-solver` concepts for static/dynamic decomposition
|
||||
//! of the CSI signal into environmental drift and body motion components.
|
||||
|
||||
/// Errors from coherence computation.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum CoherenceError {
|
||||
/// Input vectors are empty.
|
||||
#[error("Empty input for coherence computation")]
|
||||
EmptyInput,
|
||||
|
||||
/// Length mismatch between current, reference, and variance vectors.
|
||||
#[error("Length mismatch: current={current}, reference={reference}, variance={variance}")]
|
||||
LengthMismatch {
|
||||
current: usize,
|
||||
reference: usize,
|
||||
variance: usize,
|
||||
},
|
||||
|
||||
/// Invalid decay rate (must be in (0, 1)).
|
||||
#[error("Invalid EMA decay rate: {0} (must be in (0, 1))")]
|
||||
InvalidDecay(f32),
|
||||
}
|
||||
|
||||
/// Drift profile classification for environmental changes.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum DriftProfile {
|
||||
/// Environment is stable (no significant baseline drift).
|
||||
Stable,
|
||||
/// Slow linear drift (temperature, humidity changes).
|
||||
Linear,
|
||||
/// Sudden step change (door opened, furniture moved).
|
||||
StepChange,
|
||||
}
|
||||
|
||||
/// Aggregate root for coherence state.
|
||||
///
|
||||
/// Maintains a running reference template (exponential moving average of
|
||||
/// accepted CSI observations) and per-subcarrier variance estimates.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CoherenceState {
|
||||
/// Per-subcarrier reference amplitude (EMA).
|
||||
reference: Vec<f32>,
|
||||
/// Per-subcarrier variance over recent window.
|
||||
variance: Vec<f32>,
|
||||
/// EMA decay rate for reference update (default 0.95).
|
||||
decay: f32,
|
||||
/// Current coherence score (0.0-1.0).
|
||||
current_score: f32,
|
||||
/// Frames since last accepted (coherent) measurement.
|
||||
stale_count: u64,
|
||||
/// Current drift profile classification.
|
||||
drift_profile: DriftProfile,
|
||||
/// Accept threshold for coherence score.
|
||||
accept_threshold: f32,
|
||||
/// Whether the reference has been initialized.
|
||||
initialized: bool,
|
||||
}
|
||||
|
||||
impl CoherenceState {
|
||||
/// Create a new coherence state for the given number of subcarriers.
|
||||
pub fn new(n_subcarriers: usize, accept_threshold: f32) -> Self {
|
||||
Self {
|
||||
reference: vec![0.0; n_subcarriers],
|
||||
variance: vec![1.0; n_subcarriers],
|
||||
decay: 0.95,
|
||||
current_score: 1.0,
|
||||
stale_count: 0,
|
||||
drift_profile: DriftProfile::Stable,
|
||||
accept_threshold,
|
||||
initialized: false,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create with a custom EMA decay rate.
|
||||
pub fn with_decay(
|
||||
n_subcarriers: usize,
|
||||
accept_threshold: f32,
|
||||
decay: f32,
|
||||
) -> std::result::Result<Self, CoherenceError> {
|
||||
if decay <= 0.0 || decay >= 1.0 {
|
||||
return Err(CoherenceError::InvalidDecay(decay));
|
||||
}
|
||||
let mut state = Self::new(n_subcarriers, accept_threshold);
|
||||
state.decay = decay;
|
||||
Ok(state)
|
||||
}
|
||||
|
||||
/// Return the current coherence score.
|
||||
pub fn score(&self) -> f32 {
|
||||
self.current_score
|
||||
}
|
||||
|
||||
/// Return the number of frames since last accepted measurement.
|
||||
pub fn stale_count(&self) -> u64 {
|
||||
self.stale_count
|
||||
}
|
||||
|
||||
/// Return the current drift profile.
|
||||
pub fn drift_profile(&self) -> DriftProfile {
|
||||
self.drift_profile
|
||||
}
|
||||
|
||||
/// Return a reference to the current reference template.
|
||||
pub fn reference(&self) -> &[f32] {
|
||||
&self.reference
|
||||
}
|
||||
|
||||
/// Return a reference to the current variance estimates.
|
||||
pub fn variance(&self) -> &[f32] {
|
||||
&self.variance
|
||||
}
|
||||
|
||||
/// Return whether the reference has been initialized.
|
||||
pub fn is_initialized(&self) -> bool {
|
||||
self.initialized
|
||||
}
|
||||
|
||||
/// Initialize the reference from a calibration observation.
|
||||
///
|
||||
/// Should be called with a static-environment CSI frame before
|
||||
/// sensing begins.
|
||||
pub fn initialize(&mut self, calibration: &[f32]) {
|
||||
self.reference = calibration.to_vec();
|
||||
self.variance = vec![1.0; calibration.len()];
|
||||
self.current_score = 1.0;
|
||||
self.stale_count = 0;
|
||||
self.initialized = true;
|
||||
}
|
||||
|
||||
/// Update the coherence state with a new observation.
|
||||
///
|
||||
/// Computes the coherence score, updates the reference template if
|
||||
/// the observation is accepted, and tracks staleness.
|
||||
pub fn update(
|
||||
&mut self,
|
||||
current: &[f32],
|
||||
) -> std::result::Result<f32, CoherenceError> {
|
||||
if current.is_empty() {
|
||||
return Err(CoherenceError::EmptyInput);
|
||||
}
|
||||
|
||||
if !self.initialized {
|
||||
self.initialize(current);
|
||||
return Ok(1.0);
|
||||
}
|
||||
|
||||
if current.len() != self.reference.len() {
|
||||
return Err(CoherenceError::LengthMismatch {
|
||||
current: current.len(),
|
||||
reference: self.reference.len(),
|
||||
variance: self.variance.len(),
|
||||
});
|
||||
}
|
||||
|
||||
// Compute coherence score
|
||||
let score = coherence_score(current, &self.reference, &self.variance);
|
||||
self.current_score = score;
|
||||
|
||||
// Update reference if accepted
|
||||
if score >= self.accept_threshold {
|
||||
self.update_reference(current);
|
||||
self.stale_count = 0;
|
||||
} else {
|
||||
self.stale_count += 1;
|
||||
}
|
||||
|
||||
// Update drift profile
|
||||
self.drift_profile = classify_drift(score, self.stale_count);
|
||||
|
||||
Ok(score)
|
||||
}
|
||||
|
||||
/// Update the reference template with EMA.
|
||||
fn update_reference(&mut self, observation: &[f32]) {
|
||||
let alpha = 1.0 - self.decay;
|
||||
for i in 0..self.reference.len() {
|
||||
let old_ref = self.reference[i];
|
||||
self.reference[i] = self.decay * old_ref + alpha * observation[i];
|
||||
|
||||
// Update variance with Welford-style online estimate
|
||||
let diff = observation[i] - old_ref;
|
||||
self.variance[i] = self.decay * self.variance[i] + alpha * diff * diff;
|
||||
// Ensure variance does not collapse to zero
|
||||
if self.variance[i] < 1e-6 {
|
||||
self.variance[i] = 1e-6;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Reset the stale counter (e.g., after recalibration).
|
||||
pub fn reset_stale(&mut self) {
|
||||
self.stale_count = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute the coherence score between a current observation and a
|
||||
/// reference template.
|
||||
///
|
||||
/// Uses z-score per subcarrier with variance-inverse weighting:
|
||||
///
|
||||
/// score = sum(w_i * exp(-0.5 * z_i^2)) / sum(w_i)
|
||||
///
|
||||
/// where z_i = |current_i - reference_i| / sqrt(variance_i)
|
||||
/// and w_i = 1 / (variance_i + epsilon).
|
||||
///
|
||||
/// Returns a value in [0.0, 1.0] where 1.0 means perfect agreement.
|
||||
pub fn coherence_score(
|
||||
current: &[f32],
|
||||
reference: &[f32],
|
||||
variance: &[f32],
|
||||
) -> f32 {
|
||||
let n = current.len().min(reference.len()).min(variance.len());
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let epsilon = 1e-6_f32;
|
||||
let mut weighted_sum = 0.0_f32;
|
||||
let mut weight_sum = 0.0_f32;
|
||||
|
||||
for i in 0..n {
|
||||
let var = variance[i].max(epsilon);
|
||||
let z = (current[i] - reference[i]).abs() / var.sqrt();
|
||||
let weight = 1.0 / (var + epsilon);
|
||||
let likelihood = (-0.5 * z * z).exp();
|
||||
weighted_sum += likelihood * weight;
|
||||
weight_sum += weight;
|
||||
}
|
||||
|
||||
if weight_sum < epsilon {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
(weighted_sum / weight_sum).clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// Classify drift profile based on coherence history.
|
||||
fn classify_drift(score: f32, stale_count: u64) -> DriftProfile {
|
||||
if score >= 0.85 {
|
||||
DriftProfile::Stable
|
||||
} else if stale_count < 10 {
|
||||
// Brief coherence loss -> likely step change
|
||||
DriftProfile::StepChange
|
||||
} else {
|
||||
// Extended low coherence -> linear drift
|
||||
DriftProfile::Linear
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute per-subcarrier z-scores for diagnostics.
|
||||
///
|
||||
/// Returns a vector of z-scores, one per subcarrier.
|
||||
pub fn per_subcarrier_zscores(
|
||||
current: &[f32],
|
||||
reference: &[f32],
|
||||
variance: &[f32],
|
||||
) -> Vec<f32> {
|
||||
let n = current.len().min(reference.len()).min(variance.len());
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let var = variance[i].max(1e-6);
|
||||
(current[i] - reference[i]).abs() / var.sqrt()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Identify subcarriers that are outliers (z-score above threshold).
|
||||
///
|
||||
/// Returns indices of outlier subcarriers.
|
||||
pub fn outlier_subcarriers(
|
||||
current: &[f32],
|
||||
reference: &[f32],
|
||||
variance: &[f32],
|
||||
z_threshold: f32,
|
||||
) -> Vec<usize> {
|
||||
let z_scores = per_subcarrier_zscores(current, reference, variance);
|
||||
z_scores
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, &z)| z > z_threshold)
|
||||
.map(|(i, _)| i)
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn perfect_coherence() {
|
||||
let current = vec![1.0, 2.0, 3.0, 4.0];
|
||||
let reference = vec![1.0, 2.0, 3.0, 4.0];
|
||||
let variance = vec![0.01, 0.01, 0.01, 0.01];
|
||||
let score = coherence_score(¤t, &reference, &variance);
|
||||
assert!((score - 1.0).abs() < 0.01, "Perfect match should give ~1.0, got {}", score);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn zero_coherence_large_deviation() {
|
||||
let current = vec![100.0, 200.0, 300.0];
|
||||
let reference = vec![0.0, 0.0, 0.0];
|
||||
let variance = vec![0.001, 0.001, 0.001];
|
||||
let score = coherence_score(¤t, &reference, &variance);
|
||||
assert!(score < 0.01, "Large deviation should give ~0.0, got {}", score);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_input_gives_zero() {
|
||||
assert_eq!(coherence_score(&[], &[], &[]), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn state_initialize_and_score() {
|
||||
let mut state = CoherenceState::new(4, 0.85);
|
||||
assert!(!state.is_initialized());
|
||||
state.initialize(&[1.0, 2.0, 3.0, 4.0]);
|
||||
assert!(state.is_initialized());
|
||||
assert!((state.score() - 1.0).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn state_update_accepted() {
|
||||
let mut state = CoherenceState::new(4, 0.5);
|
||||
state.initialize(&[1.0, 2.0, 3.0, 4.0]);
|
||||
let score = state.update(&[1.01, 2.01, 3.01, 4.01]).unwrap();
|
||||
assert!(score > 0.8, "Small deviation should be accepted, got {}", score);
|
||||
assert_eq!(state.stale_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn state_update_rejected() {
|
||||
let mut state = CoherenceState::new(4, 0.99);
|
||||
state.initialize(&[1.0, 2.0, 3.0, 4.0]);
|
||||
let _ = state.update(&[10.0, 20.0, 30.0, 40.0]).unwrap();
|
||||
assert!(state.stale_count() > 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn auto_initialize_on_first_update() {
|
||||
let mut state = CoherenceState::new(3, 0.85);
|
||||
let score = state.update(&[5.0, 6.0, 7.0]).unwrap();
|
||||
assert!((score - 1.0).abs() < f32::EPSILON);
|
||||
assert!(state.is_initialized());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn length_mismatch_error() {
|
||||
let mut state = CoherenceState::new(4, 0.85);
|
||||
state.initialize(&[1.0, 2.0, 3.0, 4.0]);
|
||||
let result = state.update(&[1.0, 2.0]);
|
||||
assert!(matches!(result, Err(CoherenceError::LengthMismatch { .. })));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_update_error() {
|
||||
let mut state = CoherenceState::new(4, 0.85);
|
||||
state.initialize(&[1.0, 2.0, 3.0, 4.0]);
|
||||
assert!(matches!(state.update(&[]), Err(CoherenceError::EmptyInput)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn invalid_decay_error() {
|
||||
assert!(matches!(
|
||||
CoherenceState::with_decay(4, 0.85, 0.0),
|
||||
Err(CoherenceError::InvalidDecay(_))
|
||||
));
|
||||
assert!(matches!(
|
||||
CoherenceState::with_decay(4, 0.85, 1.0),
|
||||
Err(CoherenceError::InvalidDecay(_))
|
||||
));
|
||||
assert!(matches!(
|
||||
CoherenceState::with_decay(4, 0.85, -0.5),
|
||||
Err(CoherenceError::InvalidDecay(_))
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn valid_decay() {
|
||||
let state = CoherenceState::with_decay(4, 0.85, 0.9).unwrap();
|
||||
assert!((state.score() - 1.0).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn drift_classification_stable() {
|
||||
assert_eq!(classify_drift(0.9, 0), DriftProfile::Stable);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn drift_classification_step_change() {
|
||||
assert_eq!(classify_drift(0.3, 5), DriftProfile::StepChange);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn drift_classification_linear() {
|
||||
assert_eq!(classify_drift(0.3, 20), DriftProfile::Linear);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn per_subcarrier_zscores_correct() {
|
||||
let current = vec![2.0, 4.0];
|
||||
let reference = vec![1.0, 2.0];
|
||||
let variance = vec![1.0, 4.0];
|
||||
let z = per_subcarrier_zscores(¤t, &reference, &variance);
|
||||
assert_eq!(z.len(), 2);
|
||||
assert!((z[0] - 1.0).abs() < 1e-5);
|
||||
assert!((z[1] - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn outlier_subcarriers_detected() {
|
||||
let current = vec![1.0, 100.0, 1.0, 200.0];
|
||||
let reference = vec![1.0, 1.0, 1.0, 1.0];
|
||||
let variance = vec![1.0, 1.0, 1.0, 1.0];
|
||||
let outliers = outlier_subcarriers(¤t, &reference, &variance, 3.0);
|
||||
assert!(outliers.contains(&1));
|
||||
assert!(outliers.contains(&3));
|
||||
assert!(!outliers.contains(&0));
|
||||
assert!(!outliers.contains(&2));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn reset_stale_counter() {
|
||||
let mut state = CoherenceState::new(4, 0.99);
|
||||
state.initialize(&[1.0, 2.0, 3.0, 4.0]);
|
||||
let _ = state.update(&[10.0, 20.0, 30.0, 40.0]).unwrap();
|
||||
assert!(state.stale_count() > 0);
|
||||
state.reset_stale();
|
||||
assert_eq!(state.stale_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn reference_and_variance_accessible() {
|
||||
let state = CoherenceState::new(3, 0.85);
|
||||
assert_eq!(state.reference().len(), 3);
|
||||
assert_eq!(state.variance().len(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_score_with_high_variance() {
|
||||
let current = vec![5.0, 6.0, 7.0];
|
||||
let reference = vec![1.0, 2.0, 3.0];
|
||||
let variance = vec![100.0, 100.0, 100.0]; // high variance
|
||||
let score = coherence_score(¤t, &reference, &variance);
|
||||
// With high variance, deviation is relatively small
|
||||
assert!(score > 0.5, "High variance should tolerate deviation, got {}", score);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,365 @@
|
||||
//! Coherence-Gated Update Policy (ADR-029 Section 2.6)
|
||||
//!
|
||||
//! Applies a threshold-based gating rule to the coherence score, producing
|
||||
//! a `GateDecision` that controls downstream Kalman filter updates:
|
||||
//!
|
||||
//! - **Accept** (coherence > 0.85): Full measurement update with nominal noise.
|
||||
//! - **PredictOnly** (0.5 < coherence < 0.85): Kalman predict step only,
|
||||
//! measurement noise inflated 3x.
|
||||
//! - **Reject** (coherence < 0.5): Discard measurement entirely.
|
||||
//! - **Recalibrate** (>10s continuous low coherence): Trigger SONA/AETHER
|
||||
//! recalibration pipeline.
|
||||
//!
|
||||
//! The gate operates on the coherence score produced by the `coherence` module
|
||||
//! and the stale frame counter from `CoherenceState`.
|
||||
|
||||
/// Gate decision controlling Kalman filter update behavior.
|
||||
#[derive(Debug, Clone, PartialEq)]
|
||||
pub enum GateDecision {
|
||||
/// Coherence is high. Proceed with full Kalman measurement update.
|
||||
/// Contains the inflated measurement noise multiplier (1.0 = nominal).
|
||||
Accept {
|
||||
/// Measurement noise multiplier (1.0 for full accept).
|
||||
noise_multiplier: f32,
|
||||
},
|
||||
|
||||
/// Coherence is moderate. Run Kalman predict only (no measurement update).
|
||||
/// Measurement noise would be inflated 3x if used.
|
||||
PredictOnly,
|
||||
|
||||
/// Coherence is low. Reject this measurement entirely.
|
||||
Reject,
|
||||
|
||||
/// Prolonged low coherence. Trigger environmental recalibration.
|
||||
/// The pipeline should freeze output at last known good pose and
|
||||
/// begin the SONA/AETHER TTT adaptation cycle.
|
||||
Recalibrate {
|
||||
/// Duration of low coherence in frames.
|
||||
stale_frames: u64,
|
||||
},
|
||||
}
|
||||
|
||||
impl GateDecision {
|
||||
/// Returns true if this decision allows a measurement update.
|
||||
pub fn allows_update(&self) -> bool {
|
||||
matches!(self, GateDecision::Accept { .. })
|
||||
}
|
||||
|
||||
/// Returns true if this is a reject or recalibrate decision.
|
||||
pub fn is_rejected(&self) -> bool {
|
||||
matches!(self, GateDecision::Reject | GateDecision::Recalibrate { .. })
|
||||
}
|
||||
|
||||
/// Returns the noise multiplier for accepted decisions, or None otherwise.
|
||||
pub fn noise_multiplier(&self) -> Option<f32> {
|
||||
match self {
|
||||
GateDecision::Accept { noise_multiplier } => Some(*noise_multiplier),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Configuration for the gate policy thresholds.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GatePolicyConfig {
|
||||
/// Coherence threshold above which measurements are accepted.
|
||||
pub accept_threshold: f32,
|
||||
/// Coherence threshold below which measurements are rejected.
|
||||
pub reject_threshold: f32,
|
||||
/// Maximum stale frames before triggering recalibration.
|
||||
pub max_stale_frames: u64,
|
||||
/// Noise inflation factor for PredictOnly zone.
|
||||
pub predict_only_noise: f32,
|
||||
/// Whether to use adaptive thresholds based on drift profile.
|
||||
pub adaptive: bool,
|
||||
}
|
||||
|
||||
impl Default for GatePolicyConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
accept_threshold: 0.85,
|
||||
reject_threshold: 0.5,
|
||||
max_stale_frames: 200, // 10s at 20Hz
|
||||
predict_only_noise: 3.0,
|
||||
adaptive: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Gate policy that maps coherence scores to gate decisions.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GatePolicy {
|
||||
/// Accept threshold.
|
||||
accept_threshold: f32,
|
||||
/// Reject threshold.
|
||||
reject_threshold: f32,
|
||||
/// Maximum stale frames before recalibration.
|
||||
max_stale_frames: u64,
|
||||
/// Noise inflation for predict-only zone.
|
||||
predict_only_noise: f32,
|
||||
/// Running count of consecutive rejected/predict-only frames.
|
||||
consecutive_low: u64,
|
||||
/// Last decision for tracking transitions.
|
||||
last_decision: Option<GateDecision>,
|
||||
}
|
||||
|
||||
impl GatePolicy {
|
||||
/// Create a gate policy with the given thresholds.
|
||||
pub fn new(accept: f32, reject: f32, max_stale: u64) -> Self {
|
||||
Self {
|
||||
accept_threshold: accept,
|
||||
reject_threshold: reject,
|
||||
max_stale_frames: max_stale,
|
||||
predict_only_noise: 3.0,
|
||||
consecutive_low: 0,
|
||||
last_decision: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a gate policy from a configuration.
|
||||
pub fn from_config(config: &GatePolicyConfig) -> Self {
|
||||
Self {
|
||||
accept_threshold: config.accept_threshold,
|
||||
reject_threshold: config.reject_threshold,
|
||||
max_stale_frames: config.max_stale_frames,
|
||||
predict_only_noise: config.predict_only_noise,
|
||||
consecutive_low: 0,
|
||||
last_decision: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Evaluate the gate decision for a given coherence score and stale count.
|
||||
pub fn evaluate(&mut self, coherence_score: f32, stale_count: u64) -> GateDecision {
|
||||
let decision = if stale_count >= self.max_stale_frames {
|
||||
GateDecision::Recalibrate {
|
||||
stale_frames: stale_count,
|
||||
}
|
||||
} else if coherence_score >= self.accept_threshold {
|
||||
self.consecutive_low = 0;
|
||||
GateDecision::Accept {
|
||||
noise_multiplier: 1.0,
|
||||
}
|
||||
} else if coherence_score >= self.reject_threshold {
|
||||
self.consecutive_low += 1;
|
||||
GateDecision::PredictOnly
|
||||
} else {
|
||||
self.consecutive_low += 1;
|
||||
GateDecision::Reject
|
||||
};
|
||||
|
||||
self.last_decision = Some(decision.clone());
|
||||
decision
|
||||
}
|
||||
|
||||
/// Return the last gate decision, if any.
|
||||
pub fn last_decision(&self) -> Option<&GateDecision> {
|
||||
self.last_decision.as_ref()
|
||||
}
|
||||
|
||||
/// Return the current count of consecutive low-coherence frames.
|
||||
pub fn consecutive_low_count(&self) -> u64 {
|
||||
self.consecutive_low
|
||||
}
|
||||
|
||||
/// Return the accept threshold.
|
||||
pub fn accept_threshold(&self) -> f32 {
|
||||
self.accept_threshold
|
||||
}
|
||||
|
||||
/// Return the reject threshold.
|
||||
pub fn reject_threshold(&self) -> f32 {
|
||||
self.reject_threshold
|
||||
}
|
||||
|
||||
/// Reset the policy state (e.g., after recalibration).
|
||||
pub fn reset(&mut self) {
|
||||
self.consecutive_low = 0;
|
||||
self.last_decision = None;
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for GatePolicy {
|
||||
fn default() -> Self {
|
||||
Self::from_config(&GatePolicyConfig::default())
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute an adaptive noise multiplier for the PredictOnly zone.
|
||||
///
|
||||
/// As coherence drops from accept to reject threshold, the noise
|
||||
/// multiplier increases from 1.0 to `max_inflation`.
|
||||
pub fn adaptive_noise_multiplier(
|
||||
coherence: f32,
|
||||
accept: f32,
|
||||
reject: f32,
|
||||
max_inflation: f32,
|
||||
) -> f32 {
|
||||
if coherence >= accept {
|
||||
return 1.0;
|
||||
}
|
||||
if coherence <= reject {
|
||||
return max_inflation;
|
||||
}
|
||||
let range = accept - reject;
|
||||
if range < 1e-6 {
|
||||
return max_inflation;
|
||||
}
|
||||
let t = (accept - coherence) / range;
|
||||
1.0 + t * (max_inflation - 1.0)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn accept_high_coherence() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.95, 0);
|
||||
assert!(matches!(decision, GateDecision::Accept { noise_multiplier } if (noise_multiplier - 1.0).abs() < f32::EPSILON));
|
||||
assert!(decision.allows_update());
|
||||
assert!(!decision.is_rejected());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn predict_only_moderate_coherence() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.7, 0);
|
||||
assert!(matches!(decision, GateDecision::PredictOnly));
|
||||
assert!(!decision.allows_update());
|
||||
assert!(!decision.is_rejected());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn reject_low_coherence() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.3, 0);
|
||||
assert!(matches!(decision, GateDecision::Reject));
|
||||
assert!(!decision.allows_update());
|
||||
assert!(decision.is_rejected());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn recalibrate_after_stale_timeout() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.3, 200);
|
||||
assert!(matches!(decision, GateDecision::Recalibrate { stale_frames: 200 }));
|
||||
assert!(decision.is_rejected());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn recalibrate_overrides_accept() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 100);
|
||||
// Even with high coherence, stale count triggers recalibration
|
||||
let decision = gate.evaluate(0.95, 100);
|
||||
assert!(matches!(decision, GateDecision::Recalibrate { .. }));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn consecutive_low_counter() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
gate.evaluate(0.3, 0);
|
||||
assert_eq!(gate.consecutive_low_count(), 1);
|
||||
gate.evaluate(0.6, 0);
|
||||
assert_eq!(gate.consecutive_low_count(), 2);
|
||||
gate.evaluate(0.9, 0); // accepted -> resets
|
||||
assert_eq!(gate.consecutive_low_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn last_decision_tracked() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
assert!(gate.last_decision().is_none());
|
||||
gate.evaluate(0.9, 0);
|
||||
assert!(gate.last_decision().is_some());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn reset_clears_state() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
gate.evaluate(0.3, 0);
|
||||
gate.evaluate(0.3, 0);
|
||||
gate.reset();
|
||||
assert_eq!(gate.consecutive_low_count(), 0);
|
||||
assert!(gate.last_decision().is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn noise_multiplier_accessor() {
|
||||
let accept = GateDecision::Accept { noise_multiplier: 2.5 };
|
||||
assert_eq!(accept.noise_multiplier(), Some(2.5));
|
||||
|
||||
let reject = GateDecision::Reject;
|
||||
assert_eq!(reject.noise_multiplier(), None);
|
||||
|
||||
let predict = GateDecision::PredictOnly;
|
||||
assert_eq!(predict.noise_multiplier(), None);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn adaptive_noise_at_boundaries() {
|
||||
assert!((adaptive_noise_multiplier(0.9, 0.85, 0.5, 3.0) - 1.0).abs() < f32::EPSILON);
|
||||
assert!((adaptive_noise_multiplier(0.3, 0.85, 0.5, 3.0) - 3.0).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn adaptive_noise_midpoint() {
|
||||
let mid = adaptive_noise_multiplier(0.675, 0.85, 0.5, 3.0);
|
||||
assert!((mid - 2.0).abs() < 0.01, "Midpoint noise should be ~2.0, got {}", mid);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn adaptive_noise_tiny_range() {
|
||||
// When accept == reject, coherence >= accept returns 1.0
|
||||
let val = adaptive_noise_multiplier(0.5, 0.5, 0.5, 3.0);
|
||||
assert!((val - 1.0).abs() < f32::EPSILON);
|
||||
// Below both thresholds should return max_inflation
|
||||
let val2 = adaptive_noise_multiplier(0.4, 0.5, 0.5, 3.0);
|
||||
assert!((val2 - 3.0).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn default_config_values() {
|
||||
let cfg = GatePolicyConfig::default();
|
||||
assert!((cfg.accept_threshold - 0.85).abs() < f32::EPSILON);
|
||||
assert!((cfg.reject_threshold - 0.5).abs() < f32::EPSILON);
|
||||
assert_eq!(cfg.max_stale_frames, 200);
|
||||
assert!((cfg.predict_only_noise - 3.0).abs() < f32::EPSILON);
|
||||
assert!(!cfg.adaptive);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn from_config_construction() {
|
||||
let cfg = GatePolicyConfig {
|
||||
accept_threshold: 0.9,
|
||||
reject_threshold: 0.4,
|
||||
max_stale_frames: 100,
|
||||
predict_only_noise: 5.0,
|
||||
adaptive: true,
|
||||
};
|
||||
let gate = GatePolicy::from_config(&cfg);
|
||||
assert!((gate.accept_threshold() - 0.9).abs() < f32::EPSILON);
|
||||
assert!((gate.reject_threshold() - 0.4).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn boundary_at_exact_accept_threshold() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.85, 0);
|
||||
assert!(matches!(decision, GateDecision::Accept { .. }));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn boundary_at_exact_reject_threshold() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.5, 0);
|
||||
assert!(matches!(decision, GateDecision::PredictOnly));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn boundary_just_below_reject_threshold() {
|
||||
let mut gate = GatePolicy::new(0.85, 0.5, 200);
|
||||
let decision = gate.evaluate(0.499, 0);
|
||||
assert!(matches!(decision, GateDecision::Reject));
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,626 @@
|
||||
//! Cross-room identity continuity.
|
||||
//!
|
||||
//! Maintains identity persistence across rooms without optics by
|
||||
//! fingerprinting each room's electromagnetic profile, tracking
|
||||
//! exit/entry events, and matching person embeddings across transition
|
||||
//! boundaries.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//! 1. Each room is fingerprinted as a 128-dim AETHER embedding of its
|
||||
//! static CSI profile
|
||||
//! 2. When a track is lost near a room boundary, record an exit event
|
||||
//! with the person's current embedding
|
||||
//! 3. When a new track appears in an adjacent room within 60s, compare
|
||||
//! its embedding against recent exits
|
||||
//! 4. If cosine similarity > 0.80, link the identities
|
||||
//!
|
||||
//! # Invariants
|
||||
//! - Cross-room match requires > 0.80 cosine similarity AND < 60s temporal gap
|
||||
//! - Transition graph is append-only (immutable audit trail)
|
||||
//! - No image data stored — only 128-dim embeddings and structural events
|
||||
//! - Maximum 100 rooms per deployment
|
||||
//!
|
||||
//! # References
|
||||
//! - ADR-030 Tier 5: Cross-Room Identity Continuity
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from cross-room operations.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum CrossRoomError {
|
||||
/// Room capacity exceeded.
|
||||
#[error("Maximum rooms exceeded: limit is {max}")]
|
||||
MaxRoomsExceeded { max: usize },
|
||||
|
||||
/// Room not found.
|
||||
#[error("Unknown room ID: {0}")]
|
||||
UnknownRoom(u64),
|
||||
|
||||
/// Embedding dimension mismatch.
|
||||
#[error("Embedding dimension mismatch: expected {expected}, got {got}")]
|
||||
EmbeddingDimensionMismatch { expected: usize, got: usize },
|
||||
|
||||
/// Invalid temporal gap for matching.
|
||||
#[error("Temporal gap {gap_s:.1}s exceeds maximum {max_s:.1}s")]
|
||||
TemporalGapExceeded { gap_s: f64, max_s: f64 },
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Configuration for cross-room identity tracking.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CrossRoomConfig {
|
||||
/// Embedding dimension (typically 128).
|
||||
pub embedding_dim: usize,
|
||||
/// Minimum cosine similarity for cross-room match.
|
||||
pub min_similarity: f32,
|
||||
/// Maximum temporal gap (seconds) for cross-room match.
|
||||
pub max_gap_s: f64,
|
||||
/// Maximum rooms in the deployment.
|
||||
pub max_rooms: usize,
|
||||
/// Maximum pending exit events to retain.
|
||||
pub max_pending_exits: usize,
|
||||
}
|
||||
|
||||
impl Default for CrossRoomConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
embedding_dim: 128,
|
||||
min_similarity: 0.80,
|
||||
max_gap_s: 60.0,
|
||||
max_rooms: 100,
|
||||
max_pending_exits: 200,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Domain types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// A room's electromagnetic fingerprint.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RoomFingerprint {
|
||||
/// Room identifier.
|
||||
pub room_id: u64,
|
||||
/// Fingerprint embedding vector.
|
||||
pub embedding: Vec<f32>,
|
||||
/// Timestamp when fingerprint was last computed (microseconds).
|
||||
pub computed_at_us: u64,
|
||||
/// Number of nodes contributing to this fingerprint.
|
||||
pub node_count: usize,
|
||||
}
|
||||
|
||||
/// An exit event: a person leaving a room.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ExitEvent {
|
||||
/// Person embedding at exit time.
|
||||
pub embedding: Vec<f32>,
|
||||
/// Room exited.
|
||||
pub room_id: u64,
|
||||
/// Person track ID (local to the room).
|
||||
pub track_id: u64,
|
||||
/// Timestamp of exit (microseconds).
|
||||
pub timestamp_us: u64,
|
||||
/// Whether this exit has been matched to an entry.
|
||||
pub matched: bool,
|
||||
}
|
||||
|
||||
/// An entry event: a person appearing in a room.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct EntryEvent {
|
||||
/// Person embedding at entry time.
|
||||
pub embedding: Vec<f32>,
|
||||
/// Room entered.
|
||||
pub room_id: u64,
|
||||
/// Person track ID (local to the room).
|
||||
pub track_id: u64,
|
||||
/// Timestamp of entry (microseconds).
|
||||
pub timestamp_us: u64,
|
||||
}
|
||||
|
||||
/// A cross-room transition record (immutable).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TransitionEvent {
|
||||
/// Person who transitioned.
|
||||
pub person_id: u64,
|
||||
/// Room exited.
|
||||
pub from_room: u64,
|
||||
/// Room entered.
|
||||
pub to_room: u64,
|
||||
/// Exit track ID.
|
||||
pub exit_track_id: u64,
|
||||
/// Entry track ID.
|
||||
pub entry_track_id: u64,
|
||||
/// Cosine similarity between exit and entry embeddings.
|
||||
pub similarity: f32,
|
||||
/// Temporal gap between exit and entry (seconds).
|
||||
pub gap_s: f64,
|
||||
/// Timestamp of the transition (entry timestamp).
|
||||
pub timestamp_us: u64,
|
||||
}
|
||||
|
||||
/// Result of attempting to match an entry against pending exits.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct MatchResult {
|
||||
/// Whether a match was found.
|
||||
pub matched: bool,
|
||||
/// The transition event, if matched.
|
||||
pub transition: Option<TransitionEvent>,
|
||||
/// Number of candidates checked.
|
||||
pub candidates_checked: usize,
|
||||
/// Best similarity found (even if below threshold).
|
||||
pub best_similarity: f32,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Cross-room identity tracker
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Cross-room identity continuity tracker.
|
||||
///
|
||||
/// Maintains room fingerprints, pending exit events, and an immutable
|
||||
/// transition graph. Matches person embeddings across rooms using
|
||||
/// cosine similarity with temporal constraints.
|
||||
#[derive(Debug)]
|
||||
pub struct CrossRoomTracker {
|
||||
config: CrossRoomConfig,
|
||||
/// Room fingerprints indexed by room_id.
|
||||
rooms: Vec<RoomFingerprint>,
|
||||
/// Pending (unmatched) exit events.
|
||||
pending_exits: Vec<ExitEvent>,
|
||||
/// Immutable transition log (append-only).
|
||||
transitions: Vec<TransitionEvent>,
|
||||
/// Next person ID for cross-room identity assignment.
|
||||
next_person_id: u64,
|
||||
}
|
||||
|
||||
impl CrossRoomTracker {
|
||||
/// Create a new cross-room tracker.
|
||||
pub fn new(config: CrossRoomConfig) -> Self {
|
||||
Self {
|
||||
config,
|
||||
rooms: Vec::new(),
|
||||
pending_exits: Vec::new(),
|
||||
transitions: Vec::new(),
|
||||
next_person_id: 1,
|
||||
}
|
||||
}
|
||||
|
||||
/// Register a room fingerprint.
|
||||
pub fn register_room(&mut self, fingerprint: RoomFingerprint) -> Result<(), CrossRoomError> {
|
||||
if self.rooms.len() >= self.config.max_rooms {
|
||||
return Err(CrossRoomError::MaxRoomsExceeded {
|
||||
max: self.config.max_rooms,
|
||||
});
|
||||
}
|
||||
if fingerprint.embedding.len() != self.config.embedding_dim {
|
||||
return Err(CrossRoomError::EmbeddingDimensionMismatch {
|
||||
expected: self.config.embedding_dim,
|
||||
got: fingerprint.embedding.len(),
|
||||
});
|
||||
}
|
||||
// Replace existing fingerprint if room already registered
|
||||
if let Some(existing) = self
|
||||
.rooms
|
||||
.iter_mut()
|
||||
.find(|r| r.room_id == fingerprint.room_id)
|
||||
{
|
||||
*existing = fingerprint;
|
||||
} else {
|
||||
self.rooms.push(fingerprint);
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Record a person exiting a room.
|
||||
pub fn record_exit(&mut self, event: ExitEvent) -> Result<(), CrossRoomError> {
|
||||
if event.embedding.len() != self.config.embedding_dim {
|
||||
return Err(CrossRoomError::EmbeddingDimensionMismatch {
|
||||
expected: self.config.embedding_dim,
|
||||
got: event.embedding.len(),
|
||||
});
|
||||
}
|
||||
// Evict oldest if at capacity
|
||||
if self.pending_exits.len() >= self.config.max_pending_exits {
|
||||
self.pending_exits.remove(0);
|
||||
}
|
||||
self.pending_exits.push(event);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Try to match an entry event against pending exits.
|
||||
///
|
||||
/// If a match is found, creates a TransitionEvent and marks the
|
||||
/// exit as matched. Returns the match result.
|
||||
pub fn match_entry(&mut self, entry: &EntryEvent) -> Result<MatchResult, CrossRoomError> {
|
||||
if entry.embedding.len() != self.config.embedding_dim {
|
||||
return Err(CrossRoomError::EmbeddingDimensionMismatch {
|
||||
expected: self.config.embedding_dim,
|
||||
got: entry.embedding.len(),
|
||||
});
|
||||
}
|
||||
|
||||
let mut best_idx: Option<usize> = None;
|
||||
let mut best_sim: f32 = -1.0;
|
||||
let mut candidates_checked = 0;
|
||||
|
||||
for (idx, exit) in self.pending_exits.iter().enumerate() {
|
||||
if exit.matched || exit.room_id == entry.room_id {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Temporal constraint
|
||||
let gap_us = entry.timestamp_us.saturating_sub(exit.timestamp_us);
|
||||
let gap_s = gap_us as f64 / 1_000_000.0;
|
||||
if gap_s > self.config.max_gap_s {
|
||||
continue;
|
||||
}
|
||||
|
||||
candidates_checked += 1;
|
||||
|
||||
let sim = cosine_similarity_f32(&exit.embedding, &entry.embedding);
|
||||
if sim > best_sim {
|
||||
best_sim = sim;
|
||||
if sim >= self.config.min_similarity {
|
||||
best_idx = Some(idx);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(idx) = best_idx {
|
||||
let exit = &self.pending_exits[idx];
|
||||
let gap_us = entry.timestamp_us.saturating_sub(exit.timestamp_us);
|
||||
let gap_s = gap_us as f64 / 1_000_000.0;
|
||||
|
||||
let person_id = self.next_person_id;
|
||||
self.next_person_id += 1;
|
||||
|
||||
let transition = TransitionEvent {
|
||||
person_id,
|
||||
from_room: exit.room_id,
|
||||
to_room: entry.room_id,
|
||||
exit_track_id: exit.track_id,
|
||||
entry_track_id: entry.track_id,
|
||||
similarity: best_sim,
|
||||
gap_s,
|
||||
timestamp_us: entry.timestamp_us,
|
||||
};
|
||||
|
||||
// Mark exit as matched
|
||||
self.pending_exits[idx].matched = true;
|
||||
|
||||
// Append to immutable transition log
|
||||
self.transitions.push(transition.clone());
|
||||
|
||||
Ok(MatchResult {
|
||||
matched: true,
|
||||
transition: Some(transition),
|
||||
candidates_checked,
|
||||
best_similarity: best_sim,
|
||||
})
|
||||
} else {
|
||||
Ok(MatchResult {
|
||||
matched: false,
|
||||
transition: None,
|
||||
candidates_checked,
|
||||
best_similarity: if best_sim >= 0.0 { best_sim } else { 0.0 },
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/// Expire old pending exits that exceed the maximum gap time.
|
||||
pub fn expire_exits(&mut self, current_us: u64) {
|
||||
let max_gap_us = (self.config.max_gap_s * 1_000_000.0) as u64;
|
||||
self.pending_exits.retain(|exit| {
|
||||
!exit.matched && current_us.saturating_sub(exit.timestamp_us) <= max_gap_us
|
||||
});
|
||||
}
|
||||
|
||||
/// Number of registered rooms.
|
||||
pub fn room_count(&self) -> usize {
|
||||
self.rooms.len()
|
||||
}
|
||||
|
||||
/// Number of pending (unmatched) exit events.
|
||||
pub fn pending_exit_count(&self) -> usize {
|
||||
self.pending_exits.iter().filter(|e| !e.matched).count()
|
||||
}
|
||||
|
||||
/// Number of transitions recorded.
|
||||
pub fn transition_count(&self) -> usize {
|
||||
self.transitions.len()
|
||||
}
|
||||
|
||||
/// Get all transitions for a person.
|
||||
pub fn transitions_for_person(&self, person_id: u64) -> Vec<&TransitionEvent> {
|
||||
self.transitions
|
||||
.iter()
|
||||
.filter(|t| t.person_id == person_id)
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Get all transitions between two rooms.
|
||||
pub fn transitions_between(&self, from_room: u64, to_room: u64) -> Vec<&TransitionEvent> {
|
||||
self.transitions
|
||||
.iter()
|
||||
.filter(|t| t.from_room == from_room && t.to_room == to_room)
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Get the room fingerprint for a room ID.
|
||||
pub fn room_fingerprint(&self, room_id: u64) -> Option<&RoomFingerprint> {
|
||||
self.rooms.iter().find(|r| r.room_id == room_id)
|
||||
}
|
||||
}
|
||||
|
||||
/// Cosine similarity between two f32 vectors.
|
||||
fn cosine_similarity_f32(a: &[f32], b: &[f32]) -> f32 {
|
||||
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
|
||||
let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let denom = norm_a * norm_b;
|
||||
if denom < 1e-9 {
|
||||
0.0
|
||||
} else {
|
||||
dot / denom
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn small_config() -> CrossRoomConfig {
|
||||
CrossRoomConfig {
|
||||
embedding_dim: 4,
|
||||
min_similarity: 0.80,
|
||||
max_gap_s: 60.0,
|
||||
max_rooms: 10,
|
||||
max_pending_exits: 50,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_fingerprint(room_id: u64, v: [f32; 4]) -> RoomFingerprint {
|
||||
RoomFingerprint {
|
||||
room_id,
|
||||
embedding: v.to_vec(),
|
||||
computed_at_us: 0,
|
||||
node_count: 4,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_exit(room_id: u64, track_id: u64, emb: [f32; 4], ts: u64) -> ExitEvent {
|
||||
ExitEvent {
|
||||
embedding: emb.to_vec(),
|
||||
room_id,
|
||||
track_id,
|
||||
timestamp_us: ts,
|
||||
matched: false,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_entry(room_id: u64, track_id: u64, emb: [f32; 4], ts: u64) -> EntryEvent {
|
||||
EntryEvent {
|
||||
embedding: emb.to_vec(),
|
||||
room_id,
|
||||
track_id,
|
||||
timestamp_us: ts,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_tracker_creation() {
|
||||
let tracker = CrossRoomTracker::new(small_config());
|
||||
assert_eq!(tracker.room_count(), 0);
|
||||
assert_eq!(tracker.pending_exit_count(), 0);
|
||||
assert_eq!(tracker.transition_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_register_room() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
tracker
|
||||
.register_room(make_fingerprint(1, [1.0, 0.0, 0.0, 0.0]))
|
||||
.unwrap();
|
||||
assert_eq!(tracker.room_count(), 1);
|
||||
assert!(tracker.room_fingerprint(1).is_some());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_max_rooms_exceeded() {
|
||||
let config = CrossRoomConfig {
|
||||
max_rooms: 2,
|
||||
..small_config()
|
||||
};
|
||||
let mut tracker = CrossRoomTracker::new(config);
|
||||
tracker
|
||||
.register_room(make_fingerprint(1, [1.0, 0.0, 0.0, 0.0]))
|
||||
.unwrap();
|
||||
tracker
|
||||
.register_room(make_fingerprint(2, [0.0, 1.0, 0.0, 0.0]))
|
||||
.unwrap();
|
||||
assert!(matches!(
|
||||
tracker.register_room(make_fingerprint(3, [0.0, 0.0, 1.0, 0.0])),
|
||||
Err(CrossRoomError::MaxRoomsExceeded { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_successful_cross_room_match() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
// Person exits room 1
|
||||
let exit_emb = [0.9, 0.1, 0.0, 0.0];
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, exit_emb, 1_000_000))
|
||||
.unwrap();
|
||||
|
||||
// Same person enters room 2 (similar embedding, within 60s)
|
||||
let entry_emb = [0.88, 0.12, 0.01, 0.0];
|
||||
let entry = make_entry(2, 200, entry_emb, 5_000_000);
|
||||
let result = tracker.match_entry(&entry).unwrap();
|
||||
|
||||
assert!(result.matched);
|
||||
let t = result.transition.unwrap();
|
||||
assert_eq!(t.from_room, 1);
|
||||
assert_eq!(t.to_room, 2);
|
||||
assert!(t.similarity >= 0.80);
|
||||
assert!(t.gap_s < 60.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_no_match_different_person() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, [1.0, 0.0, 0.0, 0.0], 1_000_000))
|
||||
.unwrap();
|
||||
|
||||
// Very different embedding
|
||||
let entry = make_entry(2, 200, [0.0, 0.0, 0.0, 1.0], 5_000_000);
|
||||
let result = tracker.match_entry(&entry).unwrap();
|
||||
|
||||
assert!(!result.matched);
|
||||
assert!(result.transition.is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_no_match_temporal_gap_exceeded() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, [1.0, 0.0, 0.0, 0.0], 0))
|
||||
.unwrap();
|
||||
|
||||
// Same embedding but 120 seconds later
|
||||
let entry = make_entry(2, 200, [1.0, 0.0, 0.0, 0.0], 120_000_000);
|
||||
let result = tracker.match_entry(&entry).unwrap();
|
||||
|
||||
assert!(!result.matched, "Should not match with > 60s gap");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_no_match_same_room() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, [1.0, 0.0, 0.0, 0.0], 1_000_000))
|
||||
.unwrap();
|
||||
|
||||
// Entry in same room should not match
|
||||
let entry = make_entry(1, 200, [1.0, 0.0, 0.0, 0.0], 2_000_000);
|
||||
let result = tracker.match_entry(&entry).unwrap();
|
||||
|
||||
assert!(!result.matched, "Same-room entry should not match");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_expire_exits() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, [1.0, 0.0, 0.0, 0.0], 0))
|
||||
.unwrap();
|
||||
tracker
|
||||
.record_exit(make_exit(2, 200, [0.0, 1.0, 0.0, 0.0], 50_000_000))
|
||||
.unwrap();
|
||||
|
||||
assert_eq!(tracker.pending_exit_count(), 2);
|
||||
|
||||
// Expire at 70s — first exit (at 0) should be expired
|
||||
tracker.expire_exits(70_000_000);
|
||||
assert_eq!(tracker.pending_exit_count(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_transition_log_immutable() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, [1.0, 0.0, 0.0, 0.0], 1_000_000))
|
||||
.unwrap();
|
||||
|
||||
let entry = make_entry(2, 200, [0.98, 0.02, 0.0, 0.0], 2_000_000);
|
||||
tracker.match_entry(&entry).unwrap();
|
||||
|
||||
assert_eq!(tracker.transition_count(), 1);
|
||||
|
||||
// More transitions should append
|
||||
tracker
|
||||
.record_exit(make_exit(2, 300, [0.0, 1.0, 0.0, 0.0], 3_000_000))
|
||||
.unwrap();
|
||||
let entry2 = make_entry(3, 400, [0.01, 0.99, 0.0, 0.0], 4_000_000);
|
||||
tracker.match_entry(&entry2).unwrap();
|
||||
|
||||
assert_eq!(tracker.transition_count(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_transitions_between_rooms() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
// Room 1 → Room 2
|
||||
tracker
|
||||
.record_exit(make_exit(1, 100, [1.0, 0.0, 0.0, 0.0], 1_000_000))
|
||||
.unwrap();
|
||||
let entry = make_entry(2, 200, [0.98, 0.02, 0.0, 0.0], 2_000_000);
|
||||
tracker.match_entry(&entry).unwrap();
|
||||
|
||||
// Room 2 → Room 3
|
||||
tracker
|
||||
.record_exit(make_exit(2, 300, [0.0, 1.0, 0.0, 0.0], 3_000_000))
|
||||
.unwrap();
|
||||
let entry2 = make_entry(3, 400, [0.01, 0.99, 0.0, 0.0], 4_000_000);
|
||||
tracker.match_entry(&entry2).unwrap();
|
||||
|
||||
let r1_r2 = tracker.transitions_between(1, 2);
|
||||
assert_eq!(r1_r2.len(), 1);
|
||||
|
||||
let r2_r3 = tracker.transitions_between(2, 3);
|
||||
assert_eq!(r2_r3.len(), 1);
|
||||
|
||||
let r1_r3 = tracker.transitions_between(1, 3);
|
||||
assert_eq!(r1_r3.len(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_dimension_mismatch() {
|
||||
let mut tracker = CrossRoomTracker::new(small_config());
|
||||
|
||||
let bad_exit = ExitEvent {
|
||||
embedding: vec![1.0, 0.0], // wrong dim
|
||||
room_id: 1,
|
||||
track_id: 1,
|
||||
timestamp_us: 0,
|
||||
matched: false,
|
||||
};
|
||||
assert!(matches!(
|
||||
tracker.record_exit(bad_exit),
|
||||
Err(CrossRoomError::EmbeddingDimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_identical() {
|
||||
let a = vec![1.0_f32, 2.0, 3.0, 4.0];
|
||||
let sim = cosine_similarity_f32(&a, &a);
|
||||
assert!((sim - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_orthogonal() {
|
||||
let a = vec![1.0_f32, 0.0, 0.0, 0.0];
|
||||
let b = vec![0.0_f32, 1.0, 0.0, 0.0];
|
||||
let sim = cosine_similarity_f32(&a, &b);
|
||||
assert!(sim.abs() < 1e-5);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,883 @@
|
||||
//! Field Normal Mode computation for persistent electromagnetic world model.
|
||||
//!
|
||||
//! The room's electromagnetic eigenstructure forms the foundation for all
|
||||
//! exotic sensing tiers. During unoccupied periods, the system learns a
|
||||
//! baseline via SVD decomposition. At runtime, observations are decomposed
|
||||
//! into environmental drift (projected onto eigenmodes) and body perturbation
|
||||
//! (the residual).
|
||||
//!
|
||||
//! # Algorithm
|
||||
//! 1. Collect CSI during empty-room calibration (>=10 min at 20 Hz)
|
||||
//! 2. Compute per-link baseline mean (Welford online accumulator)
|
||||
//! 3. Decompose covariance via SVD to extract environmental modes
|
||||
//! 4. At runtime: observation - baseline, project out top-K modes, keep residual
|
||||
//!
|
||||
//! # References
|
||||
//! - Welford, B.P. (1962). "Note on a Method for Calculating Corrected Sums
|
||||
//! of Squares and Products." Technometrics.
|
||||
//! - ADR-030: RuvSense Persistent Field Model
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from field model operations.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum FieldModelError {
|
||||
/// Not enough calibration frames collected.
|
||||
#[error("Insufficient calibration frames: need {needed}, got {got}")]
|
||||
InsufficientCalibration { needed: usize, got: usize },
|
||||
|
||||
/// Dimensionality mismatch between observation and baseline.
|
||||
#[error("Dimension mismatch: baseline has {expected} subcarriers, observation has {got}")]
|
||||
DimensionMismatch { expected: usize, got: usize },
|
||||
|
||||
/// SVD computation failed.
|
||||
#[error("SVD computation failed: {0}")]
|
||||
SvdFailed(String),
|
||||
|
||||
/// No links configured for the field model.
|
||||
#[error("No links configured")]
|
||||
NoLinks,
|
||||
|
||||
/// Baseline has expired and needs recalibration.
|
||||
#[error("Baseline expired: calibrated {elapsed_s:.1}s ago, max {max_s:.1}s")]
|
||||
BaselineExpired { elapsed_s: f64, max_s: f64 },
|
||||
|
||||
/// Invalid configuration parameter.
|
||||
#[error("Invalid configuration: {0}")]
|
||||
InvalidConfig(String),
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Welford online statistics (f64 precision for accumulation)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Welford's online algorithm for computing running mean and variance.
|
||||
///
|
||||
/// Maintains numerically stable incremental statistics without storing
|
||||
/// all observations. Uses f64 for accumulation precision even when
|
||||
/// runtime values are f32.
|
||||
///
|
||||
/// # References
|
||||
/// Welford (1962), Knuth TAOCP Vol 2 Section 4.2.2.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct WelfordStats {
|
||||
/// Number of observations accumulated.
|
||||
pub count: u64,
|
||||
/// Running mean.
|
||||
pub mean: f64,
|
||||
/// Running sum of squared deviations (M2).
|
||||
pub m2: f64,
|
||||
}
|
||||
|
||||
impl WelfordStats {
|
||||
/// Create a new empty accumulator.
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
count: 0,
|
||||
mean: 0.0,
|
||||
m2: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Add a new observation.
|
||||
pub fn update(&mut self, value: f64) {
|
||||
self.count += 1;
|
||||
let delta = value - self.mean;
|
||||
self.mean += delta / self.count as f64;
|
||||
let delta2 = value - self.mean;
|
||||
self.m2 += delta * delta2;
|
||||
}
|
||||
|
||||
/// Population variance (biased). Returns 0.0 if count < 2.
|
||||
pub fn variance(&self) -> f64 {
|
||||
if self.count < 2 {
|
||||
0.0
|
||||
} else {
|
||||
self.m2 / self.count as f64
|
||||
}
|
||||
}
|
||||
|
||||
/// Population standard deviation.
|
||||
pub fn std_dev(&self) -> f64 {
|
||||
self.variance().sqrt()
|
||||
}
|
||||
|
||||
/// Sample variance (unbiased). Returns 0.0 if count < 2.
|
||||
pub fn sample_variance(&self) -> f64 {
|
||||
if self.count < 2 {
|
||||
0.0
|
||||
} else {
|
||||
self.m2 / (self.count - 1) as f64
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute z-score of a value against accumulated statistics.
|
||||
/// Returns 0.0 if standard deviation is near zero.
|
||||
pub fn z_score(&self, value: f64) -> f64 {
|
||||
let sd = self.std_dev();
|
||||
if sd < 1e-15 {
|
||||
0.0
|
||||
} else {
|
||||
(value - self.mean) / sd
|
||||
}
|
||||
}
|
||||
|
||||
/// Merge two Welford accumulators (parallel Welford).
|
||||
pub fn merge(&mut self, other: &WelfordStats) {
|
||||
if other.count == 0 {
|
||||
return;
|
||||
}
|
||||
if self.count == 0 {
|
||||
*self = other.clone();
|
||||
return;
|
||||
}
|
||||
let total = self.count + other.count;
|
||||
let delta = other.mean - self.mean;
|
||||
let combined_mean = self.mean + delta * (other.count as f64 / total as f64);
|
||||
let combined_m2 = self.m2
|
||||
+ other.m2
|
||||
+ delta * delta * (self.count as f64 * other.count as f64 / total as f64);
|
||||
self.count = total;
|
||||
self.mean = combined_mean;
|
||||
self.m2 = combined_m2;
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for WelfordStats {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Multivariate Welford for per-subcarrier statistics
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Per-subcarrier Welford accumulator for a single link.
|
||||
///
|
||||
/// Tracks independent running mean and variance for each subcarrier
|
||||
/// on a given TX-RX link.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct LinkBaselineStats {
|
||||
/// Per-subcarrier accumulators.
|
||||
pub subcarriers: Vec<WelfordStats>,
|
||||
}
|
||||
|
||||
impl LinkBaselineStats {
|
||||
/// Create accumulators for `n_subcarriers`.
|
||||
pub fn new(n_subcarriers: usize) -> Self {
|
||||
Self {
|
||||
subcarriers: (0..n_subcarriers).map(|_| WelfordStats::new()).collect(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Number of subcarriers tracked.
|
||||
pub fn n_subcarriers(&self) -> usize {
|
||||
self.subcarriers.len()
|
||||
}
|
||||
|
||||
/// Update with a new CSI amplitude observation for this link.
|
||||
/// `amplitudes` must have the same length as `n_subcarriers`.
|
||||
pub fn update(&mut self, amplitudes: &[f64]) -> Result<(), FieldModelError> {
|
||||
if amplitudes.len() != self.subcarriers.len() {
|
||||
return Err(FieldModelError::DimensionMismatch {
|
||||
expected: self.subcarriers.len(),
|
||||
got: amplitudes.len(),
|
||||
});
|
||||
}
|
||||
for (stats, &) in self.subcarriers.iter_mut().zip(amplitudes.iter()) {
|
||||
stats.update(amp);
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Extract the baseline mean vector.
|
||||
pub fn mean_vector(&self) -> Vec<f64> {
|
||||
self.subcarriers.iter().map(|s| s.mean).collect()
|
||||
}
|
||||
|
||||
/// Extract the variance vector.
|
||||
pub fn variance_vector(&self) -> Vec<f64> {
|
||||
self.subcarriers.iter().map(|s| s.variance()).collect()
|
||||
}
|
||||
|
||||
/// Number of observations accumulated.
|
||||
pub fn observation_count(&self) -> u64 {
|
||||
self.subcarriers.first().map_or(0, |s| s.count)
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Field Normal Mode
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Configuration for field model calibration and runtime.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct FieldModelConfig {
|
||||
/// Number of links in the mesh.
|
||||
pub n_links: usize,
|
||||
/// Number of subcarriers per link.
|
||||
pub n_subcarriers: usize,
|
||||
/// Number of environmental modes to retain (K). Max 5.
|
||||
pub n_modes: usize,
|
||||
/// Minimum calibration frames before baseline is valid (10 min at 20 Hz = 12000).
|
||||
pub min_calibration_frames: usize,
|
||||
/// Baseline expiry in seconds (default 86400 = 24 hours).
|
||||
pub baseline_expiry_s: f64,
|
||||
}
|
||||
|
||||
impl Default for FieldModelConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
n_links: 6,
|
||||
n_subcarriers: 56,
|
||||
n_modes: 3,
|
||||
min_calibration_frames: 12_000,
|
||||
baseline_expiry_s: 86_400.0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Electromagnetic eigenstructure of a room.
|
||||
///
|
||||
/// Learned from SVD on the covariance of CSI amplitudes during
|
||||
/// empty-room calibration. The top-K modes capture environmental
|
||||
/// variation (temperature, humidity, time-of-day effects).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct FieldNormalMode {
|
||||
/// Per-link baseline mean: `[n_links][n_subcarriers]`.
|
||||
pub baseline: Vec<Vec<f64>>,
|
||||
/// Environmental eigenmodes: `[n_modes][n_subcarriers]`.
|
||||
/// Each mode is an orthonormal vector in subcarrier space.
|
||||
pub environmental_modes: Vec<Vec<f64>>,
|
||||
/// Eigenvalues (mode energies), sorted descending.
|
||||
pub mode_energies: Vec<f64>,
|
||||
/// Fraction of total variance explained by retained modes.
|
||||
pub variance_explained: f64,
|
||||
/// Timestamp (microseconds) when calibration completed.
|
||||
pub calibrated_at_us: u64,
|
||||
/// Hash of mesh geometry at calibration time.
|
||||
pub geometry_hash: u64,
|
||||
}
|
||||
|
||||
/// Body perturbation extracted from a CSI observation.
|
||||
///
|
||||
/// After subtracting the baseline and projecting out environmental
|
||||
/// modes, the residual captures structured changes caused by people
|
||||
/// in the room.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BodyPerturbation {
|
||||
/// Per-link residual amplitudes: `[n_links][n_subcarriers]`.
|
||||
pub residuals: Vec<Vec<f64>>,
|
||||
/// Per-link perturbation energy (L2 norm of residual).
|
||||
pub energies: Vec<f64>,
|
||||
/// Total perturbation energy across all links.
|
||||
pub total_energy: f64,
|
||||
/// Per-link environmental projection magnitude.
|
||||
pub environmental_projections: Vec<f64>,
|
||||
}
|
||||
|
||||
/// Calibration status of the field model.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum CalibrationStatus {
|
||||
/// No calibration data yet.
|
||||
Uncalibrated,
|
||||
/// Collecting calibration frames.
|
||||
Collecting,
|
||||
/// Calibration complete and fresh.
|
||||
Fresh,
|
||||
/// Calibration older than half expiry.
|
||||
Stale,
|
||||
/// Calibration has expired.
|
||||
Expired,
|
||||
}
|
||||
|
||||
/// The persistent field model for a single room.
|
||||
///
|
||||
/// Maintains per-link Welford statistics during calibration, then
|
||||
/// computes SVD to extract environmental modes. At runtime, decomposes
|
||||
/// observations into environmental drift and body perturbation.
|
||||
#[derive(Debug)]
|
||||
pub struct FieldModel {
|
||||
config: FieldModelConfig,
|
||||
/// Per-link calibration statistics.
|
||||
link_stats: Vec<LinkBaselineStats>,
|
||||
/// Computed field normal modes (None until calibration completes).
|
||||
modes: Option<FieldNormalMode>,
|
||||
/// Current calibration status.
|
||||
status: CalibrationStatus,
|
||||
/// Timestamp of last calibration completion (microseconds).
|
||||
last_calibration_us: u64,
|
||||
}
|
||||
|
||||
impl FieldModel {
|
||||
/// Create a new field model for the given configuration.
|
||||
pub fn new(config: FieldModelConfig) -> Result<Self, FieldModelError> {
|
||||
if config.n_links == 0 {
|
||||
return Err(FieldModelError::NoLinks);
|
||||
}
|
||||
if config.n_modes > 5 {
|
||||
return Err(FieldModelError::InvalidConfig(
|
||||
"n_modes must be <= 5 to avoid overfitting".into(),
|
||||
));
|
||||
}
|
||||
if config.n_subcarriers == 0 {
|
||||
return Err(FieldModelError::InvalidConfig(
|
||||
"n_subcarriers must be > 0".into(),
|
||||
));
|
||||
}
|
||||
|
||||
let link_stats = (0..config.n_links)
|
||||
.map(|_| LinkBaselineStats::new(config.n_subcarriers))
|
||||
.collect();
|
||||
|
||||
Ok(Self {
|
||||
config,
|
||||
link_stats,
|
||||
modes: None,
|
||||
status: CalibrationStatus::Uncalibrated,
|
||||
last_calibration_us: 0,
|
||||
})
|
||||
}
|
||||
|
||||
/// Current calibration status.
|
||||
pub fn status(&self) -> CalibrationStatus {
|
||||
self.status
|
||||
}
|
||||
|
||||
/// Access the computed field normal modes, if available.
|
||||
pub fn modes(&self) -> Option<&FieldNormalMode> {
|
||||
self.modes.as_ref()
|
||||
}
|
||||
|
||||
/// Number of calibration frames collected so far.
|
||||
pub fn calibration_frame_count(&self) -> u64 {
|
||||
self.link_stats
|
||||
.first()
|
||||
.map_or(0, |ls| ls.observation_count())
|
||||
}
|
||||
|
||||
/// Feed a calibration frame (one CSI observation per link during empty room).
|
||||
///
|
||||
/// `observations` is `[n_links][n_subcarriers]` amplitude data.
|
||||
pub fn feed_calibration(&mut self, observations: &[Vec<f64>]) -> Result<(), FieldModelError> {
|
||||
if observations.len() != self.config.n_links {
|
||||
return Err(FieldModelError::DimensionMismatch {
|
||||
expected: self.config.n_links,
|
||||
got: observations.len(),
|
||||
});
|
||||
}
|
||||
for (link_stat, obs) in self.link_stats.iter_mut().zip(observations.iter()) {
|
||||
link_stat.update(obs)?;
|
||||
}
|
||||
if self.status == CalibrationStatus::Uncalibrated {
|
||||
self.status = CalibrationStatus::Collecting;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Finalize calibration: compute SVD to extract environmental modes.
|
||||
///
|
||||
/// Requires at least `min_calibration_frames` observations.
|
||||
/// `timestamp_us` is the current timestamp in microseconds.
|
||||
/// `geometry_hash` identifies the mesh geometry at calibration time.
|
||||
pub fn finalize_calibration(
|
||||
&mut self,
|
||||
timestamp_us: u64,
|
||||
geometry_hash: u64,
|
||||
) -> Result<&FieldNormalMode, FieldModelError> {
|
||||
let count = self.calibration_frame_count();
|
||||
if count < self.config.min_calibration_frames as u64 {
|
||||
return Err(FieldModelError::InsufficientCalibration {
|
||||
needed: self.config.min_calibration_frames,
|
||||
got: count as usize,
|
||||
});
|
||||
}
|
||||
|
||||
// Build covariance matrix from per-link variance data.
|
||||
// We average the variance vectors across all links to get the
|
||||
// covariance diagonal, then compute eigenmodes via power iteration.
|
||||
let n_sc = self.config.n_subcarriers;
|
||||
let n_modes = self.config.n_modes.min(n_sc);
|
||||
|
||||
// Collect per-link baselines
|
||||
let baseline: Vec<Vec<f64>> = self.link_stats.iter().map(|ls| ls.mean_vector()).collect();
|
||||
|
||||
// Average covariance across links (diagonal approximation)
|
||||
let mut avg_variance = vec![0.0_f64; n_sc];
|
||||
for ls in &self.link_stats {
|
||||
let var = ls.variance_vector();
|
||||
for (i, v) in var.iter().enumerate() {
|
||||
avg_variance[i] += v;
|
||||
}
|
||||
}
|
||||
let n_links_f = self.config.n_links as f64;
|
||||
for v in avg_variance.iter_mut() {
|
||||
*v /= n_links_f;
|
||||
}
|
||||
|
||||
// Extract modes via simplified power iteration on the diagonal
|
||||
// covariance. Since we use a diagonal approximation, the eigenmodes
|
||||
// are aligned with the standard basis, sorted by variance.
|
||||
let total_variance: f64 = avg_variance.iter().sum();
|
||||
|
||||
// Sort subcarrier indices by variance (descending) to pick top-K modes
|
||||
let mut indices: Vec<usize> = (0..n_sc).collect();
|
||||
indices.sort_by(|&a, &b| {
|
||||
avg_variance[b]
|
||||
.partial_cmp(&avg_variance[a])
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
|
||||
let mut environmental_modes = Vec::with_capacity(n_modes);
|
||||
let mut mode_energies = Vec::with_capacity(n_modes);
|
||||
let mut explained = 0.0_f64;
|
||||
|
||||
for k in 0..n_modes {
|
||||
let idx = indices[k];
|
||||
// Create a unit vector along the highest-variance subcarrier
|
||||
let mut mode = vec![0.0_f64; n_sc];
|
||||
mode[idx] = 1.0;
|
||||
let energy = avg_variance[idx];
|
||||
environmental_modes.push(mode);
|
||||
mode_energies.push(energy);
|
||||
explained += energy;
|
||||
}
|
||||
|
||||
let variance_explained = if total_variance > 1e-15 {
|
||||
explained / total_variance
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
let field_mode = FieldNormalMode {
|
||||
baseline,
|
||||
environmental_modes,
|
||||
mode_energies,
|
||||
variance_explained,
|
||||
calibrated_at_us: timestamp_us,
|
||||
geometry_hash,
|
||||
};
|
||||
|
||||
self.modes = Some(field_mode);
|
||||
self.status = CalibrationStatus::Fresh;
|
||||
self.last_calibration_us = timestamp_us;
|
||||
|
||||
Ok(self.modes.as_ref().unwrap())
|
||||
}
|
||||
|
||||
/// Extract body perturbation from a runtime observation.
|
||||
///
|
||||
/// Subtracts baseline, projects out environmental modes, returns residual.
|
||||
/// `observations` is `[n_links][n_subcarriers]` amplitude data.
|
||||
pub fn extract_perturbation(
|
||||
&self,
|
||||
observations: &[Vec<f64>],
|
||||
) -> Result<BodyPerturbation, FieldModelError> {
|
||||
let modes = self
|
||||
.modes
|
||||
.as_ref()
|
||||
.ok_or(FieldModelError::InsufficientCalibration {
|
||||
needed: self.config.min_calibration_frames,
|
||||
got: 0,
|
||||
})?;
|
||||
|
||||
if observations.len() != self.config.n_links {
|
||||
return Err(FieldModelError::DimensionMismatch {
|
||||
expected: self.config.n_links,
|
||||
got: observations.len(),
|
||||
});
|
||||
}
|
||||
|
||||
let n_sc = self.config.n_subcarriers;
|
||||
let mut residuals = Vec::with_capacity(self.config.n_links);
|
||||
let mut energies = Vec::with_capacity(self.config.n_links);
|
||||
let mut environmental_projections = Vec::with_capacity(self.config.n_links);
|
||||
|
||||
for (link_idx, obs) in observations.iter().enumerate() {
|
||||
if obs.len() != n_sc {
|
||||
return Err(FieldModelError::DimensionMismatch {
|
||||
expected: n_sc,
|
||||
got: obs.len(),
|
||||
});
|
||||
}
|
||||
|
||||
// Step 1: subtract baseline
|
||||
let mut residual = vec![0.0_f64; n_sc];
|
||||
for i in 0..n_sc {
|
||||
residual[i] = obs[i] - modes.baseline[link_idx][i];
|
||||
}
|
||||
|
||||
// Step 2: project out environmental modes
|
||||
let mut env_proj_magnitude = 0.0_f64;
|
||||
for mode in &modes.environmental_modes {
|
||||
// Inner product of residual with mode
|
||||
let projection: f64 = residual.iter().zip(mode.iter()).map(|(r, m)| r * m).sum();
|
||||
env_proj_magnitude += projection.abs();
|
||||
|
||||
// Subtract projection
|
||||
for i in 0..n_sc {
|
||||
residual[i] -= projection * mode[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Step 3: compute energy (L2 norm)
|
||||
let energy: f64 = residual.iter().map(|r| r * r).sum::<f64>().sqrt();
|
||||
|
||||
environmental_projections.push(env_proj_magnitude);
|
||||
energies.push(energy);
|
||||
residuals.push(residual);
|
||||
}
|
||||
|
||||
let total_energy: f64 = energies.iter().sum();
|
||||
|
||||
Ok(BodyPerturbation {
|
||||
residuals,
|
||||
energies,
|
||||
total_energy,
|
||||
environmental_projections,
|
||||
})
|
||||
}
|
||||
|
||||
/// Check calibration freshness against a given timestamp.
|
||||
pub fn check_freshness(&self, current_us: u64) -> CalibrationStatus {
|
||||
if self.modes.is_none() {
|
||||
return CalibrationStatus::Uncalibrated;
|
||||
}
|
||||
let elapsed_s = current_us.saturating_sub(self.last_calibration_us) as f64 / 1_000_000.0;
|
||||
if elapsed_s > self.config.baseline_expiry_s {
|
||||
CalibrationStatus::Expired
|
||||
} else if elapsed_s > self.config.baseline_expiry_s * 0.5 {
|
||||
CalibrationStatus::Stale
|
||||
} else {
|
||||
CalibrationStatus::Fresh
|
||||
}
|
||||
}
|
||||
|
||||
/// Reset calibration and begin collecting again.
|
||||
pub fn reset_calibration(&mut self) {
|
||||
self.link_stats = (0..self.config.n_links)
|
||||
.map(|_| LinkBaselineStats::new(self.config.n_subcarriers))
|
||||
.collect();
|
||||
self.modes = None;
|
||||
self.status = CalibrationStatus::Uncalibrated;
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_config(n_links: usize, n_sc: usize, min_frames: usize) -> FieldModelConfig {
|
||||
FieldModelConfig {
|
||||
n_links,
|
||||
n_subcarriers: n_sc,
|
||||
n_modes: 3,
|
||||
min_calibration_frames: min_frames,
|
||||
baseline_expiry_s: 86_400.0,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_observations(n_links: usize, n_sc: usize, base: f64) -> Vec<Vec<f64>> {
|
||||
(0..n_links)
|
||||
.map(|l| {
|
||||
(0..n_sc)
|
||||
.map(|s| base + 0.1 * l as f64 + 0.01 * s as f64)
|
||||
.collect()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_welford_basic() {
|
||||
let mut w = WelfordStats::new();
|
||||
for v in &[2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0] {
|
||||
w.update(*v);
|
||||
}
|
||||
assert!((w.mean - 5.0).abs() < 1e-10);
|
||||
assert!((w.variance() - 4.0).abs() < 1e-10);
|
||||
assert_eq!(w.count, 8);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_welford_z_score() {
|
||||
let mut w = WelfordStats::new();
|
||||
for v in 0..100 {
|
||||
w.update(v as f64);
|
||||
}
|
||||
let z = w.z_score(w.mean);
|
||||
assert!(z.abs() < 1e-10, "z-score of mean should be 0");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_welford_merge() {
|
||||
let mut a = WelfordStats::new();
|
||||
let mut b = WelfordStats::new();
|
||||
for v in 0..50 {
|
||||
a.update(v as f64);
|
||||
}
|
||||
for v in 50..100 {
|
||||
b.update(v as f64);
|
||||
}
|
||||
a.merge(&b);
|
||||
assert_eq!(a.count, 100);
|
||||
assert!((a.mean - 49.5).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_welford_single_value() {
|
||||
let mut w = WelfordStats::new();
|
||||
w.update(42.0);
|
||||
assert_eq!(w.count, 1);
|
||||
assert!((w.mean - 42.0).abs() < 1e-10);
|
||||
assert!((w.variance() - 0.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_link_baseline_stats() {
|
||||
let mut stats = LinkBaselineStats::new(4);
|
||||
stats.update(&[1.0, 2.0, 3.0, 4.0]).unwrap();
|
||||
stats.update(&[2.0, 3.0, 4.0, 5.0]).unwrap();
|
||||
|
||||
let mean = stats.mean_vector();
|
||||
assert!((mean[0] - 1.5).abs() < 1e-10);
|
||||
assert!((mean[3] - 4.5).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_link_baseline_dimension_mismatch() {
|
||||
let mut stats = LinkBaselineStats::new(4);
|
||||
let result = stats.update(&[1.0, 2.0]);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_field_model_creation() {
|
||||
let config = make_config(6, 56, 100);
|
||||
let model = FieldModel::new(config).unwrap();
|
||||
assert_eq!(model.status(), CalibrationStatus::Uncalibrated);
|
||||
assert!(model.modes().is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_field_model_no_links_error() {
|
||||
let config = FieldModelConfig {
|
||||
n_links: 0,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(matches!(
|
||||
FieldModel::new(config),
|
||||
Err(FieldModelError::NoLinks)
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_field_model_too_many_modes() {
|
||||
let config = FieldModelConfig {
|
||||
n_modes: 6,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(matches!(
|
||||
FieldModel::new(config),
|
||||
Err(FieldModelError::InvalidConfig(_))
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_calibration_flow() {
|
||||
let config = make_config(2, 4, 10);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
// Feed calibration frames
|
||||
for i in 0..10 {
|
||||
let obs = make_observations(2, 4, 1.0 + 0.01 * i as f64);
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
|
||||
assert_eq!(model.status(), CalibrationStatus::Collecting);
|
||||
assert_eq!(model.calibration_frame_count(), 10);
|
||||
|
||||
// Finalize
|
||||
let modes = model.finalize_calibration(1_000_000, 0xDEAD).unwrap();
|
||||
assert_eq!(modes.environmental_modes.len(), 3);
|
||||
assert!(modes.variance_explained > 0.0);
|
||||
assert_eq!(model.status(), CalibrationStatus::Fresh);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_calibration_insufficient_frames() {
|
||||
let config = make_config(2, 4, 100);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
for i in 0..5 {
|
||||
let obs = make_observations(2, 4, 1.0 + 0.01 * i as f64);
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
|
||||
assert!(matches!(
|
||||
model.finalize_calibration(1_000_000, 0),
|
||||
Err(FieldModelError::InsufficientCalibration { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_perturbation_extraction() {
|
||||
let config = make_config(2, 4, 5);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
// Calibrate with baseline
|
||||
for _ in 0..5 {
|
||||
let obs = make_observations(2, 4, 1.0);
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(1_000_000, 0).unwrap();
|
||||
|
||||
// Observe with a perturbation on top of baseline
|
||||
let mut perturbed = make_observations(2, 4, 1.0);
|
||||
perturbed[0][2] += 5.0; // big perturbation on link 0, subcarrier 2
|
||||
|
||||
let perturbation = model.extract_perturbation(&perturbed).unwrap();
|
||||
assert!(perturbation.total_energy > 0.0);
|
||||
assert!(perturbation.energies[0] > perturbation.energies[1]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_perturbation_baseline_observation_same() {
|
||||
let config = make_config(2, 4, 5);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
let obs = make_observations(2, 4, 1.0);
|
||||
for _ in 0..5 {
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(1_000_000, 0).unwrap();
|
||||
|
||||
let perturbation = model.extract_perturbation(&obs).unwrap();
|
||||
assert!(
|
||||
perturbation.total_energy < 0.01,
|
||||
"Same-as-baseline should yield near-zero perturbation"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_perturbation_dimension_mismatch() {
|
||||
let config = make_config(2, 4, 5);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
let obs = make_observations(2, 4, 1.0);
|
||||
for _ in 0..5 {
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(1_000_000, 0).unwrap();
|
||||
|
||||
// Wrong number of links
|
||||
let wrong_obs = make_observations(3, 4, 1.0);
|
||||
assert!(model.extract_perturbation(&wrong_obs).is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_calibration_freshness() {
|
||||
let config = make_config(2, 4, 5);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
let obs = make_observations(2, 4, 1.0);
|
||||
for _ in 0..5 {
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(0, 0).unwrap();
|
||||
|
||||
assert_eq!(model.check_freshness(0), CalibrationStatus::Fresh);
|
||||
// 12 hours later: stale
|
||||
let twelve_hours_us = 12 * 3600 * 1_000_000;
|
||||
assert_eq!(
|
||||
model.check_freshness(twelve_hours_us),
|
||||
CalibrationStatus::Fresh
|
||||
);
|
||||
// 13 hours later: stale (> 50% of 24h)
|
||||
let thirteen_hours_us = 13 * 3600 * 1_000_000;
|
||||
assert_eq!(
|
||||
model.check_freshness(thirteen_hours_us),
|
||||
CalibrationStatus::Stale
|
||||
);
|
||||
// 25 hours later: expired
|
||||
let twentyfive_hours_us = 25 * 3600 * 1_000_000;
|
||||
assert_eq!(
|
||||
model.check_freshness(twentyfive_hours_us),
|
||||
CalibrationStatus::Expired
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_reset_calibration() {
|
||||
let config = make_config(2, 4, 5);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
let obs = make_observations(2, 4, 1.0);
|
||||
for _ in 0..5 {
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(1_000_000, 0).unwrap();
|
||||
assert!(model.modes().is_some());
|
||||
|
||||
model.reset_calibration();
|
||||
assert!(model.modes().is_none());
|
||||
assert_eq!(model.status(), CalibrationStatus::Uncalibrated);
|
||||
assert_eq!(model.calibration_frame_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_environmental_modes_sorted_by_energy() {
|
||||
let config = make_config(1, 8, 5);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
// Create observations with high variance on subcarrier 3
|
||||
for i in 0..20 {
|
||||
let mut obs = vec![vec![1.0; 8]];
|
||||
obs[0][3] += (i as f64) * 0.5; // high variance
|
||||
obs[0][7] += (i as f64) * 0.1; // lower variance
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(1_000_000, 0).unwrap();
|
||||
|
||||
let modes = model.modes().unwrap();
|
||||
// Eigenvalues should be in descending order
|
||||
for w in modes.mode_energies.windows(2) {
|
||||
assert!(w[0] >= w[1], "Mode energies must be descending");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_environmental_projection_removes_drift() {
|
||||
let config = make_config(1, 4, 10);
|
||||
let mut model = FieldModel::new(config).unwrap();
|
||||
|
||||
// Calibrate with drift on subcarrier 0
|
||||
for i in 0..10 {
|
||||
let obs = vec![vec![
|
||||
1.0 + 0.5 * i as f64, // drifting
|
||||
2.0,
|
||||
3.0,
|
||||
4.0,
|
||||
]];
|
||||
model.feed_calibration(&obs).unwrap();
|
||||
}
|
||||
model.finalize_calibration(1_000_000, 0).unwrap();
|
||||
|
||||
// Observe with same drift pattern (no body)
|
||||
let obs = vec![vec![1.0 + 0.5 * 5.0, 2.0, 3.0, 4.0]];
|
||||
let perturbation = model.extract_perturbation(&obs).unwrap();
|
||||
|
||||
// The drift on subcarrier 0 should be mostly captured by
|
||||
// environmental modes, leaving small residual
|
||||
assert!(
|
||||
perturbation.environmental_projections[0] > 0.0,
|
||||
"Environmental projection should be non-zero for drifting subcarrier"
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,579 @@
|
||||
//! Gesture classification from per-person CSI perturbation patterns.
|
||||
//!
|
||||
//! Classifies gestures by comparing per-person CSI perturbation time
|
||||
//! series against a library of gesture templates using Dynamic Time
|
||||
//! Warping (DTW). Works through walls and darkness because it operates
|
||||
//! on RF perturbations, not visual features.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//! 1. Collect per-person CSI perturbation over a gesture window (~1s)
|
||||
//! 2. Normalize and project onto principal components
|
||||
//! 3. Compare against stored gesture templates using DTW distance
|
||||
//! 4. Classify as the nearest template if distance < threshold
|
||||
//!
|
||||
//! # Supported Gestures
|
||||
//! Wave, point, beckon, push, circle, plus custom user-defined templates.
|
||||
//!
|
||||
//! # References
|
||||
//! - ADR-030 Tier 6: Invisible Interaction Layer
|
||||
//! - Sakoe & Chiba (1978), "Dynamic programming algorithm optimization
|
||||
//! for spoken word recognition" IEEE TASSP
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from gesture classification.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum GestureError {
|
||||
/// Gesture sequence too short.
|
||||
#[error("Sequence too short: need >= {needed} frames, got {got}")]
|
||||
SequenceTooShort { needed: usize, got: usize },
|
||||
|
||||
/// No templates registered for classification.
|
||||
#[error("No gesture templates registered")]
|
||||
NoTemplates,
|
||||
|
||||
/// Feature dimension mismatch.
|
||||
#[error("Feature dimension mismatch: expected {expected}, got {got}")]
|
||||
DimensionMismatch { expected: usize, got: usize },
|
||||
|
||||
/// Invalid template name.
|
||||
#[error("Invalid template name: {0}")]
|
||||
InvalidTemplateName(String),
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Domain types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Built-in gesture categories.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
|
||||
pub enum GestureType {
|
||||
/// Waving hand (side to side).
|
||||
Wave,
|
||||
/// Pointing at a target.
|
||||
Point,
|
||||
/// Beckoning (come here).
|
||||
Beckon,
|
||||
/// Push forward motion.
|
||||
Push,
|
||||
/// Circular motion.
|
||||
Circle,
|
||||
/// User-defined custom gesture.
|
||||
Custom,
|
||||
}
|
||||
|
||||
impl GestureType {
|
||||
/// Human-readable name.
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
GestureType::Wave => "wave",
|
||||
GestureType::Point => "point",
|
||||
GestureType::Beckon => "beckon",
|
||||
GestureType::Push => "push",
|
||||
GestureType::Circle => "circle",
|
||||
GestureType::Custom => "custom",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A gesture template: a reference time series for a known gesture.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GestureTemplate {
|
||||
/// Unique template name (e.g., "wave_right", "push_forward").
|
||||
pub name: String,
|
||||
/// Gesture category.
|
||||
pub gesture_type: GestureType,
|
||||
/// Template feature sequence: `[n_frames][feature_dim]`.
|
||||
pub sequence: Vec<Vec<f64>>,
|
||||
/// Feature dimension.
|
||||
pub feature_dim: usize,
|
||||
}
|
||||
|
||||
/// Result of gesture classification.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GestureResult {
|
||||
/// Whether a gesture was recognized.
|
||||
pub recognized: bool,
|
||||
/// Matched gesture type (if recognized).
|
||||
pub gesture_type: Option<GestureType>,
|
||||
/// Matched template name (if recognized).
|
||||
pub template_name: Option<String>,
|
||||
/// DTW distance to best match.
|
||||
pub distance: f64,
|
||||
/// Confidence (0.0 to 1.0, based on relative distances).
|
||||
pub confidence: f64,
|
||||
/// Person ID this gesture belongs to.
|
||||
pub person_id: u64,
|
||||
/// Timestamp (microseconds).
|
||||
pub timestamp_us: u64,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Configuration for the gesture classifier.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GestureConfig {
|
||||
/// Feature dimension of perturbation vectors.
|
||||
pub feature_dim: usize,
|
||||
/// Minimum sequence length (frames) for a valid gesture.
|
||||
pub min_sequence_len: usize,
|
||||
/// Maximum DTW distance for a match (lower = stricter).
|
||||
pub max_distance: f64,
|
||||
/// DTW Sakoe-Chiba band width (constrains warping).
|
||||
pub band_width: usize,
|
||||
}
|
||||
|
||||
impl Default for GestureConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
feature_dim: 8,
|
||||
min_sequence_len: 10,
|
||||
max_distance: 50.0,
|
||||
band_width: 5,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Gesture classifier
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Gesture classifier using DTW template matching.
|
||||
///
|
||||
/// Maintains a library of gesture templates and classifies new
|
||||
/// perturbation sequences by finding the nearest template.
|
||||
#[derive(Debug)]
|
||||
pub struct GestureClassifier {
|
||||
config: GestureConfig,
|
||||
templates: Vec<GestureTemplate>,
|
||||
}
|
||||
|
||||
impl GestureClassifier {
|
||||
/// Create a new gesture classifier.
|
||||
pub fn new(config: GestureConfig) -> Self {
|
||||
Self {
|
||||
config,
|
||||
templates: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Register a gesture template.
|
||||
pub fn add_template(&mut self, template: GestureTemplate) -> Result<(), GestureError> {
|
||||
if template.name.is_empty() {
|
||||
return Err(GestureError::InvalidTemplateName(
|
||||
"Template name cannot be empty".into(),
|
||||
));
|
||||
}
|
||||
if template.feature_dim != self.config.feature_dim {
|
||||
return Err(GestureError::DimensionMismatch {
|
||||
expected: self.config.feature_dim,
|
||||
got: template.feature_dim,
|
||||
});
|
||||
}
|
||||
if template.sequence.len() < self.config.min_sequence_len {
|
||||
return Err(GestureError::SequenceTooShort {
|
||||
needed: self.config.min_sequence_len,
|
||||
got: template.sequence.len(),
|
||||
});
|
||||
}
|
||||
self.templates.push(template);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Number of registered templates.
|
||||
pub fn template_count(&self) -> usize {
|
||||
self.templates.len()
|
||||
}
|
||||
|
||||
/// Classify a perturbation sequence against registered templates.
|
||||
///
|
||||
/// `sequence` is `[n_frames][feature_dim]` of perturbation features.
|
||||
pub fn classify(
|
||||
&self,
|
||||
sequence: &[Vec<f64>],
|
||||
person_id: u64,
|
||||
timestamp_us: u64,
|
||||
) -> Result<GestureResult, GestureError> {
|
||||
if self.templates.is_empty() {
|
||||
return Err(GestureError::NoTemplates);
|
||||
}
|
||||
if sequence.len() < self.config.min_sequence_len {
|
||||
return Err(GestureError::SequenceTooShort {
|
||||
needed: self.config.min_sequence_len,
|
||||
got: sequence.len(),
|
||||
});
|
||||
}
|
||||
// Validate feature dimension
|
||||
for frame in sequence {
|
||||
if frame.len() != self.config.feature_dim {
|
||||
return Err(GestureError::DimensionMismatch {
|
||||
expected: self.config.feature_dim,
|
||||
got: frame.len(),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Compute DTW distance to each template
|
||||
let mut best_dist = f64::INFINITY;
|
||||
let mut second_best_dist = f64::INFINITY;
|
||||
let mut best_idx: Option<usize> = None;
|
||||
|
||||
for (idx, template) in self.templates.iter().enumerate() {
|
||||
let dist = dtw_distance(sequence, &template.sequence, self.config.band_width);
|
||||
if dist < best_dist {
|
||||
second_best_dist = best_dist;
|
||||
best_dist = dist;
|
||||
best_idx = Some(idx);
|
||||
} else if dist < second_best_dist {
|
||||
second_best_dist = dist;
|
||||
}
|
||||
}
|
||||
|
||||
let recognized = best_dist <= self.config.max_distance;
|
||||
|
||||
// Confidence: how much better is the best match vs second best
|
||||
let confidence = if recognized && second_best_dist.is_finite() && second_best_dist > 1e-10 {
|
||||
(1.0 - best_dist / second_best_dist).clamp(0.0, 1.0)
|
||||
} else if recognized {
|
||||
(1.0 - best_dist / self.config.max_distance).clamp(0.0, 1.0)
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
if let Some(idx) = best_idx {
|
||||
let template = &self.templates[idx];
|
||||
Ok(GestureResult {
|
||||
recognized,
|
||||
gesture_type: if recognized {
|
||||
Some(template.gesture_type)
|
||||
} else {
|
||||
None
|
||||
},
|
||||
template_name: if recognized {
|
||||
Some(template.name.clone())
|
||||
} else {
|
||||
None
|
||||
},
|
||||
distance: best_dist,
|
||||
confidence,
|
||||
person_id,
|
||||
timestamp_us,
|
||||
})
|
||||
} else {
|
||||
Ok(GestureResult {
|
||||
recognized: false,
|
||||
gesture_type: None,
|
||||
template_name: None,
|
||||
distance: f64::INFINITY,
|
||||
confidence: 0.0,
|
||||
person_id,
|
||||
timestamp_us,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Dynamic Time Warping
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Compute DTW distance between two multivariate time series.
|
||||
///
|
||||
/// Uses the Sakoe-Chiba band constraint to limit warping.
|
||||
/// Each frame is a vector of `feature_dim` dimensions.
|
||||
fn dtw_distance(seq_a: &[Vec<f64>], seq_b: &[Vec<f64>], band_width: usize) -> f64 {
|
||||
let n = seq_a.len();
|
||||
let m = seq_b.len();
|
||||
|
||||
if n == 0 || m == 0 {
|
||||
return f64::INFINITY;
|
||||
}
|
||||
|
||||
// Cost matrix (only need 2 rows for memory efficiency)
|
||||
let mut prev = vec![f64::INFINITY; m + 1];
|
||||
let mut curr = vec![f64::INFINITY; m + 1];
|
||||
prev[0] = 0.0;
|
||||
|
||||
for i in 1..=n {
|
||||
curr[0] = f64::INFINITY;
|
||||
|
||||
let j_start = if band_width >= i {
|
||||
1
|
||||
} else {
|
||||
i.saturating_sub(band_width).max(1)
|
||||
};
|
||||
let j_end = (i + band_width).min(m);
|
||||
|
||||
for j in 1..=m {
|
||||
if j < j_start || j > j_end {
|
||||
curr[j] = f64::INFINITY;
|
||||
continue;
|
||||
}
|
||||
|
||||
let cost = euclidean_distance(&seq_a[i - 1], &seq_b[j - 1]);
|
||||
curr[j] = cost
|
||||
+ prev[j] // insertion
|
||||
.min(curr[j - 1]) // deletion
|
||||
.min(prev[j - 1]); // match
|
||||
}
|
||||
|
||||
std::mem::swap(&mut prev, &mut curr);
|
||||
}
|
||||
|
||||
prev[m]
|
||||
}
|
||||
|
||||
/// Euclidean distance between two feature vectors.
|
||||
fn euclidean_distance(a: &[f64], b: &[f64]) -> f64 {
|
||||
a.iter()
|
||||
.zip(b.iter())
|
||||
.map(|(x, y)| (x - y) * (x - y))
|
||||
.sum::<f64>()
|
||||
.sqrt()
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_template(
|
||||
name: &str,
|
||||
gesture_type: GestureType,
|
||||
n_frames: usize,
|
||||
feature_dim: usize,
|
||||
pattern: fn(usize, usize) -> f64,
|
||||
) -> GestureTemplate {
|
||||
let sequence: Vec<Vec<f64>> = (0..n_frames)
|
||||
.map(|t| (0..feature_dim).map(|d| pattern(t, d)).collect())
|
||||
.collect();
|
||||
GestureTemplate {
|
||||
name: name.to_string(),
|
||||
gesture_type,
|
||||
sequence,
|
||||
feature_dim,
|
||||
}
|
||||
}
|
||||
|
||||
fn wave_pattern(t: usize, d: usize) -> f64 {
|
||||
if d == 0 {
|
||||
(t as f64 * 0.5).sin()
|
||||
} else {
|
||||
0.0
|
||||
}
|
||||
}
|
||||
|
||||
fn push_pattern(t: usize, d: usize) -> f64 {
|
||||
if d == 0 {
|
||||
t as f64 * 0.1
|
||||
} else {
|
||||
0.0
|
||||
}
|
||||
}
|
||||
|
||||
fn small_config() -> GestureConfig {
|
||||
GestureConfig {
|
||||
feature_dim: 4,
|
||||
min_sequence_len: 5,
|
||||
max_distance: 10.0,
|
||||
band_width: 3,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_classifier_creation() {
|
||||
let classifier = GestureClassifier::new(small_config());
|
||||
assert_eq!(classifier.template_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_template() {
|
||||
let mut classifier = GestureClassifier::new(small_config());
|
||||
let template = make_template("wave", GestureType::Wave, 10, 4, wave_pattern);
|
||||
classifier.add_template(template).unwrap();
|
||||
assert_eq!(classifier.template_count(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_template_empty_name() {
|
||||
let mut classifier = GestureClassifier::new(small_config());
|
||||
let template = make_template("", GestureType::Wave, 10, 4, wave_pattern);
|
||||
assert!(matches!(
|
||||
classifier.add_template(template),
|
||||
Err(GestureError::InvalidTemplateName(_))
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_template_wrong_dim() {
|
||||
let mut classifier = GestureClassifier::new(small_config());
|
||||
let template = make_template("wave", GestureType::Wave, 10, 8, wave_pattern);
|
||||
assert!(matches!(
|
||||
classifier.add_template(template),
|
||||
Err(GestureError::DimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_template_too_short() {
|
||||
let mut classifier = GestureClassifier::new(small_config());
|
||||
let template = make_template("wave", GestureType::Wave, 3, 4, wave_pattern);
|
||||
assert!(matches!(
|
||||
classifier.add_template(template),
|
||||
Err(GestureError::SequenceTooShort { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_classify_no_templates() {
|
||||
let classifier = GestureClassifier::new(small_config());
|
||||
let seq: Vec<Vec<f64>> = (0..10).map(|_| vec![0.0; 4]).collect();
|
||||
assert!(matches!(
|
||||
classifier.classify(&seq, 1, 0),
|
||||
Err(GestureError::NoTemplates)
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_classify_exact_match() {
|
||||
let mut classifier = GestureClassifier::new(small_config());
|
||||
let template = make_template("wave", GestureType::Wave, 10, 4, wave_pattern);
|
||||
classifier.add_template(template).unwrap();
|
||||
|
||||
// Feed the exact same pattern
|
||||
let seq: Vec<Vec<f64>> = (0..10)
|
||||
.map(|t| (0..4).map(|d| wave_pattern(t, d)).collect())
|
||||
.collect();
|
||||
|
||||
let result = classifier.classify(&seq, 1, 100_000).unwrap();
|
||||
assert!(result.recognized);
|
||||
assert_eq!(result.gesture_type, Some(GestureType::Wave));
|
||||
assert!(
|
||||
result.distance < 1e-10,
|
||||
"Exact match should have zero distance"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_classify_best_of_two() {
|
||||
let mut classifier = GestureClassifier::new(GestureConfig {
|
||||
max_distance: 100.0,
|
||||
..small_config()
|
||||
});
|
||||
classifier
|
||||
.add_template(make_template(
|
||||
"wave",
|
||||
GestureType::Wave,
|
||||
10,
|
||||
4,
|
||||
wave_pattern,
|
||||
))
|
||||
.unwrap();
|
||||
classifier
|
||||
.add_template(make_template(
|
||||
"push",
|
||||
GestureType::Push,
|
||||
10,
|
||||
4,
|
||||
push_pattern,
|
||||
))
|
||||
.unwrap();
|
||||
|
||||
// Feed a wave-like pattern
|
||||
let seq: Vec<Vec<f64>> = (0..10)
|
||||
.map(|t| (0..4).map(|d| wave_pattern(t, d) + 0.01).collect())
|
||||
.collect();
|
||||
|
||||
let result = classifier.classify(&seq, 1, 0).unwrap();
|
||||
assert!(result.recognized);
|
||||
assert_eq!(result.gesture_type, Some(GestureType::Wave));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_classify_no_match_high_distance() {
|
||||
let mut classifier = GestureClassifier::new(GestureConfig {
|
||||
max_distance: 0.001, // very strict
|
||||
..small_config()
|
||||
});
|
||||
classifier
|
||||
.add_template(make_template(
|
||||
"wave",
|
||||
GestureType::Wave,
|
||||
10,
|
||||
4,
|
||||
wave_pattern,
|
||||
))
|
||||
.unwrap();
|
||||
|
||||
// Random-ish sequence
|
||||
let seq: Vec<Vec<f64>> = (0..10)
|
||||
.map(|t| vec![t as f64 * 10.0, 0.0, 0.0, 0.0])
|
||||
.collect();
|
||||
|
||||
let result = classifier.classify(&seq, 1, 0).unwrap();
|
||||
assert!(!result.recognized);
|
||||
assert!(result.gesture_type.is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_dtw_identical_sequences() {
|
||||
let seq: Vec<Vec<f64>> = vec![vec![1.0, 2.0], vec![3.0, 4.0], vec![5.0, 6.0]];
|
||||
let dist = dtw_distance(&seq, &seq, 3);
|
||||
assert!(
|
||||
dist < 1e-10,
|
||||
"Identical sequences should have zero DTW distance"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_dtw_different_sequences() {
|
||||
let a: Vec<Vec<f64>> = vec![vec![0.0], vec![0.0], vec![0.0]];
|
||||
let b: Vec<Vec<f64>> = vec![vec![10.0], vec![10.0], vec![10.0]];
|
||||
let dist = dtw_distance(&a, &b, 3);
|
||||
assert!(
|
||||
dist > 0.0,
|
||||
"Different sequences should have non-zero DTW distance"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_dtw_time_warped() {
|
||||
// Same shape but different speed
|
||||
let a: Vec<Vec<f64>> = vec![vec![0.0], vec![1.0], vec![2.0], vec![3.0]];
|
||||
let b: Vec<Vec<f64>> = vec![
|
||||
vec![0.0],
|
||||
vec![0.5],
|
||||
vec![1.0],
|
||||
vec![1.5],
|
||||
vec![2.0],
|
||||
vec![2.5],
|
||||
vec![3.0],
|
||||
];
|
||||
let dist = dtw_distance(&a, &b, 4);
|
||||
// DTW should be relatively small despite different lengths
|
||||
assert!(dist < 2.0, "DTW should handle time warping, got {}", dist);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_euclidean_distance() {
|
||||
let a = vec![0.0, 3.0];
|
||||
let b = vec![4.0, 0.0];
|
||||
let d = euclidean_distance(&a, &b);
|
||||
assert!((d - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_gesture_type_names() {
|
||||
assert_eq!(GestureType::Wave.name(), "wave");
|
||||
assert_eq!(GestureType::Push.name(), "push");
|
||||
assert_eq!(GestureType::Circle.name(), "circle");
|
||||
assert_eq!(GestureType::Custom.name(), "custom");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,509 @@
|
||||
//! Pre-movement intention lead signal detector.
|
||||
//!
|
||||
//! Detects anticipatory postural adjustments (APAs) 200-500ms before
|
||||
//! visible movement onset. Works by analyzing the trajectory of AETHER
|
||||
//! embeddings in embedding space: before a person initiates a step or
|
||||
//! reach, their weight shifts create subtle CSI changes that appear as
|
||||
//! velocity and acceleration in embedding space.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//! 1. Maintain a rolling window of recent embeddings (2 seconds at 20 Hz)
|
||||
//! 2. Compute velocity (first derivative) and acceleration (second derivative)
|
||||
//! in embedding space
|
||||
//! 3. Detect when acceleration exceeds a threshold while velocity is still low
|
||||
//! (the body is loading/shifting but hasn't moved yet)
|
||||
//! 4. Output a lead signal with estimated time-to-movement
|
||||
//!
|
||||
//! # References
|
||||
//! - ADR-030 Tier 3: Intention Lead Signals
|
||||
//! - Massion (1992), "Movement, posture and equilibrium: Interaction
|
||||
//! and coordination" Progress in Neurobiology
|
||||
|
||||
use std::collections::VecDeque;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from intention detection operations.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum IntentionError {
|
||||
/// Not enough embedding history to compute derivatives.
|
||||
#[error("Insufficient history: need >= {needed} frames, got {got}")]
|
||||
InsufficientHistory { needed: usize, got: usize },
|
||||
|
||||
/// Embedding dimension mismatch.
|
||||
#[error("Embedding dimension mismatch: expected {expected}, got {got}")]
|
||||
DimensionMismatch { expected: usize, got: usize },
|
||||
|
||||
/// Invalid configuration.
|
||||
#[error("Invalid configuration: {0}")]
|
||||
InvalidConfig(String),
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Configuration for the intention detector.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct IntentionConfig {
|
||||
/// Embedding dimension (typically 128).
|
||||
pub embedding_dim: usize,
|
||||
/// Rolling window size in frames (2s at 20Hz = 40 frames).
|
||||
pub window_size: usize,
|
||||
/// Sampling rate in Hz.
|
||||
pub sample_rate_hz: f64,
|
||||
/// Acceleration threshold for pre-movement detection (embedding space units/s^2).
|
||||
pub acceleration_threshold: f64,
|
||||
/// Maximum velocity for a pre-movement signal (below this = still preparing).
|
||||
pub max_pre_movement_velocity: f64,
|
||||
/// Minimum frames of sustained acceleration to trigger a lead signal.
|
||||
pub min_sustained_frames: usize,
|
||||
/// Lead time window: max seconds before movement that we flag.
|
||||
pub max_lead_time_s: f64,
|
||||
}
|
||||
|
||||
impl Default for IntentionConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
embedding_dim: 128,
|
||||
window_size: 40,
|
||||
sample_rate_hz: 20.0,
|
||||
acceleration_threshold: 0.5,
|
||||
max_pre_movement_velocity: 2.0,
|
||||
min_sustained_frames: 4,
|
||||
max_lead_time_s: 0.5,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Lead signal result
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Pre-movement lead signal.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct LeadSignal {
|
||||
/// Whether a pre-movement signal was detected.
|
||||
pub detected: bool,
|
||||
/// Confidence in the detection (0.0 to 1.0).
|
||||
pub confidence: f64,
|
||||
/// Estimated time until movement onset (seconds).
|
||||
pub estimated_lead_time_s: f64,
|
||||
/// Current velocity magnitude in embedding space.
|
||||
pub velocity_magnitude: f64,
|
||||
/// Current acceleration magnitude in embedding space.
|
||||
pub acceleration_magnitude: f64,
|
||||
/// Number of consecutive frames of sustained acceleration.
|
||||
pub sustained_frames: usize,
|
||||
/// Timestamp (microseconds) of this detection.
|
||||
pub timestamp_us: u64,
|
||||
/// Dominant direction of acceleration (unit vector in embedding space, first 3 dims).
|
||||
pub direction_hint: [f64; 3],
|
||||
}
|
||||
|
||||
/// Trajectory state for one frame.
|
||||
#[derive(Debug, Clone)]
|
||||
struct TrajectoryPoint {
|
||||
embedding: Vec<f64>,
|
||||
timestamp_us: u64,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Intention detector
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Pre-movement intention lead signal detector.
|
||||
///
|
||||
/// Maintains a rolling window of embeddings and computes velocity
|
||||
/// and acceleration in embedding space to detect anticipatory
|
||||
/// postural adjustments before movement onset.
|
||||
#[derive(Debug)]
|
||||
pub struct IntentionDetector {
|
||||
config: IntentionConfig,
|
||||
/// Rolling window of recent trajectory points.
|
||||
history: VecDeque<TrajectoryPoint>,
|
||||
/// Count of consecutive frames with pre-movement signature.
|
||||
sustained_count: usize,
|
||||
/// Total frames processed.
|
||||
total_frames: u64,
|
||||
}
|
||||
|
||||
impl IntentionDetector {
|
||||
/// Create a new intention detector.
|
||||
pub fn new(config: IntentionConfig) -> Result<Self, IntentionError> {
|
||||
if config.embedding_dim == 0 {
|
||||
return Err(IntentionError::InvalidConfig(
|
||||
"embedding_dim must be > 0".into(),
|
||||
));
|
||||
}
|
||||
if config.window_size < 3 {
|
||||
return Err(IntentionError::InvalidConfig(
|
||||
"window_size must be >= 3 for second derivative".into(),
|
||||
));
|
||||
}
|
||||
Ok(Self {
|
||||
history: VecDeque::with_capacity(config.window_size),
|
||||
config,
|
||||
sustained_count: 0,
|
||||
total_frames: 0,
|
||||
})
|
||||
}
|
||||
|
||||
/// Feed a new embedding and check for pre-movement signals.
|
||||
///
|
||||
/// `embedding` is the AETHER embedding for the current frame.
|
||||
/// Returns a lead signal result.
|
||||
pub fn update(
|
||||
&mut self,
|
||||
embedding: &[f32],
|
||||
timestamp_us: u64,
|
||||
) -> Result<LeadSignal, IntentionError> {
|
||||
if embedding.len() != self.config.embedding_dim {
|
||||
return Err(IntentionError::DimensionMismatch {
|
||||
expected: self.config.embedding_dim,
|
||||
got: embedding.len(),
|
||||
});
|
||||
}
|
||||
|
||||
self.total_frames += 1;
|
||||
|
||||
// Convert to f64 for trajectory analysis
|
||||
let emb_f64: Vec<f64> = embedding.iter().map(|&x| x as f64).collect();
|
||||
|
||||
// Add to history
|
||||
if self.history.len() >= self.config.window_size {
|
||||
self.history.pop_front();
|
||||
}
|
||||
self.history.push_back(TrajectoryPoint {
|
||||
embedding: emb_f64,
|
||||
timestamp_us,
|
||||
});
|
||||
|
||||
// Need at least 3 points for second derivative
|
||||
if self.history.len() < 3 {
|
||||
return Ok(LeadSignal {
|
||||
detected: false,
|
||||
confidence: 0.0,
|
||||
estimated_lead_time_s: 0.0,
|
||||
velocity_magnitude: 0.0,
|
||||
acceleration_magnitude: 0.0,
|
||||
sustained_frames: 0,
|
||||
timestamp_us,
|
||||
direction_hint: [0.0; 3],
|
||||
});
|
||||
}
|
||||
|
||||
// Compute velocity and acceleration
|
||||
let n = self.history.len();
|
||||
let dt = 1.0 / self.config.sample_rate_hz;
|
||||
|
||||
// Velocity: (embedding[n-1] - embedding[n-2]) / dt
|
||||
let velocity = embedding_diff(
|
||||
&self.history[n - 1].embedding,
|
||||
&self.history[n - 2].embedding,
|
||||
dt,
|
||||
);
|
||||
let velocity_mag = l2_norm_f64(&velocity);
|
||||
|
||||
// Acceleration: (velocity[n-1] - velocity[n-2]) / dt
|
||||
// Approximate: (emb[n-1] - 2*emb[n-2] + emb[n-3]) / dt^2
|
||||
let acceleration = embedding_second_diff(
|
||||
&self.history[n - 1].embedding,
|
||||
&self.history[n - 2].embedding,
|
||||
&self.history[n - 3].embedding,
|
||||
dt,
|
||||
);
|
||||
let accel_mag = l2_norm_f64(&acceleration);
|
||||
|
||||
// Pre-movement detection:
|
||||
// High acceleration + low velocity = body is loading/shifting but hasn't moved
|
||||
let is_pre_movement = accel_mag > self.config.acceleration_threshold
|
||||
&& velocity_mag < self.config.max_pre_movement_velocity;
|
||||
|
||||
if is_pre_movement {
|
||||
self.sustained_count += 1;
|
||||
} else {
|
||||
self.sustained_count = 0;
|
||||
}
|
||||
|
||||
let detected = self.sustained_count >= self.config.min_sustained_frames;
|
||||
|
||||
// Estimate lead time based on current acceleration and velocity
|
||||
let estimated_lead = if detected && accel_mag > 1e-10 {
|
||||
// Time until velocity reaches threshold: t = (v_thresh - v) / a
|
||||
let remaining = (self.config.max_pre_movement_velocity - velocity_mag) / accel_mag;
|
||||
remaining.clamp(0.0, self.config.max_lead_time_s)
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
// Confidence based on how clearly the acceleration exceeds threshold
|
||||
let confidence = if detected {
|
||||
let ratio = accel_mag / self.config.acceleration_threshold;
|
||||
(ratio - 1.0).clamp(0.0, 1.0)
|
||||
* (self.sustained_count as f64 / self.config.min_sustained_frames as f64).min(1.0)
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
// Direction hint from first 3 dimensions of acceleration
|
||||
let direction_hint = [
|
||||
acceleration.first().copied().unwrap_or(0.0),
|
||||
acceleration.get(1).copied().unwrap_or(0.0),
|
||||
acceleration.get(2).copied().unwrap_or(0.0),
|
||||
];
|
||||
|
||||
Ok(LeadSignal {
|
||||
detected,
|
||||
confidence,
|
||||
estimated_lead_time_s: estimated_lead,
|
||||
velocity_magnitude: velocity_mag,
|
||||
acceleration_magnitude: accel_mag,
|
||||
sustained_frames: self.sustained_count,
|
||||
timestamp_us,
|
||||
direction_hint,
|
||||
})
|
||||
}
|
||||
|
||||
/// Reset the detector state.
|
||||
pub fn reset(&mut self) {
|
||||
self.history.clear();
|
||||
self.sustained_count = 0;
|
||||
}
|
||||
|
||||
/// Number of frames in the history.
|
||||
pub fn history_len(&self) -> usize {
|
||||
self.history.len()
|
||||
}
|
||||
|
||||
/// Total frames processed.
|
||||
pub fn total_frames(&self) -> u64 {
|
||||
self.total_frames
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Utility functions
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// First difference of two embedding vectors, divided by dt.
|
||||
fn embedding_diff(a: &[f64], b: &[f64], dt: f64) -> Vec<f64> {
|
||||
a.iter()
|
||||
.zip(b.iter())
|
||||
.map(|(&ai, &bi)| (ai - bi) / dt)
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Second difference: (a - 2b + c) / dt^2.
|
||||
fn embedding_second_diff(a: &[f64], b: &[f64], c: &[f64], dt: f64) -> Vec<f64> {
|
||||
let dt2 = dt * dt;
|
||||
a.iter()
|
||||
.zip(b.iter())
|
||||
.zip(c.iter())
|
||||
.map(|((&ai, &bi), &ci)| (ai - 2.0 * bi + ci) / dt2)
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// L2 norm of an f64 slice.
|
||||
fn l2_norm_f64(v: &[f64]) -> f64 {
|
||||
v.iter().map(|x| x * x).sum::<f64>().sqrt()
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_config() -> IntentionConfig {
|
||||
IntentionConfig {
|
||||
embedding_dim: 4,
|
||||
window_size: 10,
|
||||
sample_rate_hz: 20.0,
|
||||
acceleration_threshold: 0.5,
|
||||
max_pre_movement_velocity: 2.0,
|
||||
min_sustained_frames: 3,
|
||||
max_lead_time_s: 0.5,
|
||||
}
|
||||
}
|
||||
|
||||
fn static_embedding() -> Vec<f32> {
|
||||
vec![1.0, 0.0, 0.0, 0.0]
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_creation() {
|
||||
let config = make_config();
|
||||
let detector = IntentionDetector::new(config).unwrap();
|
||||
assert_eq!(detector.history_len(), 0);
|
||||
assert_eq!(detector.total_frames(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_invalid_config_zero_dim() {
|
||||
let config = IntentionConfig {
|
||||
embedding_dim: 0,
|
||||
..make_config()
|
||||
};
|
||||
assert!(matches!(
|
||||
IntentionDetector::new(config),
|
||||
Err(IntentionError::InvalidConfig(_))
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_invalid_config_small_window() {
|
||||
let config = IntentionConfig {
|
||||
window_size: 2,
|
||||
..make_config()
|
||||
};
|
||||
assert!(matches!(
|
||||
IntentionDetector::new(config),
|
||||
Err(IntentionError::InvalidConfig(_))
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_dimension_mismatch() {
|
||||
let config = make_config();
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
let result = detector.update(&[1.0, 0.0], 0);
|
||||
assert!(matches!(
|
||||
result,
|
||||
Err(IntentionError::DimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_static_scene_no_detection() {
|
||||
let config = make_config();
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
|
||||
for frame in 0..20 {
|
||||
let signal = detector
|
||||
.update(&static_embedding(), frame * 50_000)
|
||||
.unwrap();
|
||||
assert!(
|
||||
!signal.detected,
|
||||
"Static scene should not trigger detection"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_gradual_acceleration_detected() {
|
||||
let mut config = make_config();
|
||||
config.acceleration_threshold = 100.0; // low threshold for test
|
||||
config.max_pre_movement_velocity = 100000.0;
|
||||
config.min_sustained_frames = 2;
|
||||
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
|
||||
// Feed gradually accelerating embeddings
|
||||
// Position = 0.5 * a * t^2, so embedding shifts quadratically
|
||||
let mut any_detected = false;
|
||||
for frame in 0..30_u64 {
|
||||
let t = frame as f32 * 0.05;
|
||||
let pos = 50.0 * t * t; // acceleration = 100 units/s^2
|
||||
let emb = vec![1.0 + pos, 0.0, 0.0, 0.0];
|
||||
let signal = detector.update(&emb, frame * 50_000).unwrap();
|
||||
if signal.detected {
|
||||
any_detected = true;
|
||||
assert!(signal.confidence > 0.0);
|
||||
assert!(signal.acceleration_magnitude > 0.0);
|
||||
}
|
||||
}
|
||||
assert!(any_detected, "Accelerating signal should trigger detection");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_constant_velocity_no_detection() {
|
||||
let config = make_config();
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
|
||||
// Constant velocity = zero acceleration → no pre-movement
|
||||
for frame in 0..20_u64 {
|
||||
let pos = frame as f32 * 0.01; // constant velocity
|
||||
let emb = vec![1.0 + pos, 0.0, 0.0, 0.0];
|
||||
let signal = detector.update(&emb, frame * 50_000).unwrap();
|
||||
assert!(
|
||||
!signal.detected,
|
||||
"Constant velocity should not trigger pre-movement"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_reset() {
|
||||
let config = make_config();
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
|
||||
for frame in 0..5_u64 {
|
||||
detector
|
||||
.update(&static_embedding(), frame * 50_000)
|
||||
.unwrap();
|
||||
}
|
||||
assert_eq!(detector.history_len(), 5);
|
||||
|
||||
detector.reset();
|
||||
assert_eq!(detector.history_len(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_lead_signal_fields() {
|
||||
let config = make_config();
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
|
||||
// Need at least 3 frames for derivatives
|
||||
for frame in 0..3_u64 {
|
||||
let signal = detector
|
||||
.update(&static_embedding(), frame * 50_000)
|
||||
.unwrap();
|
||||
assert_eq!(signal.sustained_frames, 0);
|
||||
}
|
||||
|
||||
let signal = detector.update(&static_embedding(), 150_000).unwrap();
|
||||
assert!(signal.velocity_magnitude >= 0.0);
|
||||
assert!(signal.acceleration_magnitude >= 0.0);
|
||||
assert_eq!(signal.direction_hint.len(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_window_size_limit() {
|
||||
let config = IntentionConfig {
|
||||
window_size: 5,
|
||||
..make_config()
|
||||
};
|
||||
let mut detector = IntentionDetector::new(config).unwrap();
|
||||
|
||||
for frame in 0..10_u64 {
|
||||
detector
|
||||
.update(&static_embedding(), frame * 50_000)
|
||||
.unwrap();
|
||||
}
|
||||
assert_eq!(detector.history_len(), 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_diff() {
|
||||
let a = vec![2.0, 4.0];
|
||||
let b = vec![1.0, 2.0];
|
||||
let diff = embedding_diff(&a, &b, 0.5);
|
||||
assert!((diff[0] - 2.0).abs() < 1e-10); // (2-1)/0.5
|
||||
assert!((diff[1] - 4.0).abs() < 1e-10); // (4-2)/0.5
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_second_diff() {
|
||||
// Quadratic sequence: 1, 4, 9 → second diff = 2
|
||||
let a = vec![9.0];
|
||||
let b = vec![4.0];
|
||||
let c = vec![1.0];
|
||||
let sd = embedding_second_diff(&a, &b, &c, 1.0);
|
||||
assert!((sd[0] - 2.0).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,676 @@
|
||||
//! Longitudinal biomechanics drift detection.
|
||||
//!
|
||||
//! Maintains per-person biophysical baselines over days/weeks using Welford
|
||||
//! online statistics. Detects meaningful drift in gait symmetry, stability,
|
||||
//! breathing regularity, micro-tremor, and activity level. Produces traceable
|
||||
//! evidence reports that link to stored embedding trajectories.
|
||||
//!
|
||||
//! # Key Invariants
|
||||
//! - Baseline requires >= 7 observation days before drift detection activates
|
||||
//! - Drift alert requires > 2-sigma deviation sustained for >= 3 consecutive days
|
||||
//! - Output is metric values and deviations, never diagnostic language
|
||||
//! - Welford statistics use full history (no windowing) for stability
|
||||
//!
|
||||
//! # References
|
||||
//! - Welford, B.P. (1962). "Note on a Method for Calculating Corrected
|
||||
//! Sums of Squares." Technometrics.
|
||||
//! - ADR-030 Tier 4: Longitudinal Biomechanics Drift
|
||||
|
||||
use crate::ruvsense::field_model::WelfordStats;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from longitudinal monitoring operations.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum LongitudinalError {
|
||||
/// Not enough observation days for drift detection.
|
||||
#[error("Insufficient observation days: need >= {needed}, got {got}")]
|
||||
InsufficientDays { needed: u32, got: u32 },
|
||||
|
||||
/// Person ID not found in the registry.
|
||||
#[error("Unknown person ID: {0}")]
|
||||
UnknownPerson(u64),
|
||||
|
||||
/// Embedding dimension mismatch.
|
||||
#[error("Embedding dimension mismatch: expected {expected}, got {got}")]
|
||||
EmbeddingDimensionMismatch { expected: usize, got: usize },
|
||||
|
||||
/// Invalid metric value.
|
||||
#[error("Invalid metric value for {metric}: {reason}")]
|
||||
InvalidMetric { metric: String, reason: String },
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Domain types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Biophysical metric types tracked per person.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
|
||||
pub enum DriftMetric {
|
||||
/// Gait symmetry ratio (0.0 = perfectly symmetric, higher = asymmetric).
|
||||
GaitSymmetry,
|
||||
/// Stability index (lower = less stable).
|
||||
StabilityIndex,
|
||||
/// Breathing regularity (coefficient of variation of breath intervals).
|
||||
BreathingRegularity,
|
||||
/// Micro-tremor amplitude (mm, from high-frequency pose jitter).
|
||||
MicroTremor,
|
||||
/// Daily activity level (normalized 0-1).
|
||||
ActivityLevel,
|
||||
}
|
||||
|
||||
impl DriftMetric {
|
||||
/// All metric variants.
|
||||
pub fn all() -> &'static [DriftMetric] {
|
||||
&[
|
||||
DriftMetric::GaitSymmetry,
|
||||
DriftMetric::StabilityIndex,
|
||||
DriftMetric::BreathingRegularity,
|
||||
DriftMetric::MicroTremor,
|
||||
DriftMetric::ActivityLevel,
|
||||
]
|
||||
}
|
||||
|
||||
/// Human-readable name.
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
DriftMetric::GaitSymmetry => "gait_symmetry",
|
||||
DriftMetric::StabilityIndex => "stability_index",
|
||||
DriftMetric::BreathingRegularity => "breathing_regularity",
|
||||
DriftMetric::MicroTremor => "micro_tremor",
|
||||
DriftMetric::ActivityLevel => "activity_level",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Direction of drift.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum DriftDirection {
|
||||
/// Metric is increasing relative to baseline.
|
||||
Increasing,
|
||||
/// Metric is decreasing relative to baseline.
|
||||
Decreasing,
|
||||
}
|
||||
|
||||
/// Monitoring level for drift reports.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
|
||||
pub enum MonitoringLevel {
|
||||
/// Level 1: Raw biophysical metric value.
|
||||
Physiological = 1,
|
||||
/// Level 2: Personal baseline deviation.
|
||||
Drift = 2,
|
||||
/// Level 3: Pattern-matched risk correlation.
|
||||
RiskCorrelation = 3,
|
||||
}
|
||||
|
||||
/// A drift report with traceable evidence.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct DriftReport {
|
||||
/// Person this report pertains to.
|
||||
pub person_id: u64,
|
||||
/// Which metric drifted.
|
||||
pub metric: DriftMetric,
|
||||
/// Direction of drift.
|
||||
pub direction: DriftDirection,
|
||||
/// Z-score relative to personal baseline.
|
||||
pub z_score: f64,
|
||||
/// Current metric value (today or most recent).
|
||||
pub current_value: f64,
|
||||
/// Baseline mean for this metric.
|
||||
pub baseline_mean: f64,
|
||||
/// Baseline standard deviation.
|
||||
pub baseline_std: f64,
|
||||
/// Number of consecutive days the drift has been sustained.
|
||||
pub sustained_days: u32,
|
||||
/// Monitoring level.
|
||||
pub level: MonitoringLevel,
|
||||
/// Timestamp (microseconds) when this report was generated.
|
||||
pub timestamp_us: u64,
|
||||
}
|
||||
|
||||
/// Daily metric summary for one person.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct DailyMetricSummary {
|
||||
/// Person ID.
|
||||
pub person_id: u64,
|
||||
/// Day timestamp (start of day, microseconds).
|
||||
pub day_us: u64,
|
||||
/// Metric values for this day.
|
||||
pub metrics: Vec<(DriftMetric, f64)>,
|
||||
/// AETHER embedding centroid for this day.
|
||||
pub embedding_centroid: Option<Vec<f32>>,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Personal baseline
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Per-person longitudinal baseline with Welford statistics.
|
||||
///
|
||||
/// Tracks running mean and variance for each biophysical metric over
|
||||
/// the person's entire observation history. Uses Welford's algorithm
|
||||
/// for numerical stability.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PersonalBaseline {
|
||||
/// Unique person identifier.
|
||||
pub person_id: u64,
|
||||
/// Per-metric Welford accumulators.
|
||||
pub gait_symmetry: WelfordStats,
|
||||
pub stability_index: WelfordStats,
|
||||
pub breathing_regularity: WelfordStats,
|
||||
pub micro_tremor: WelfordStats,
|
||||
pub activity_level: WelfordStats,
|
||||
/// Running centroid of AETHER embeddings.
|
||||
pub embedding_centroid: Vec<f32>,
|
||||
/// Number of observation days.
|
||||
pub observation_days: u32,
|
||||
/// Timestamp of last update (microseconds).
|
||||
pub updated_at_us: u64,
|
||||
/// Per-metric consecutive drift days counter.
|
||||
drift_counters: [u32; 5],
|
||||
}
|
||||
|
||||
impl PersonalBaseline {
|
||||
/// Create a new baseline for a person.
|
||||
///
|
||||
/// `embedding_dim` is typically 128 for AETHER embeddings.
|
||||
pub fn new(person_id: u64, embedding_dim: usize) -> Self {
|
||||
Self {
|
||||
person_id,
|
||||
gait_symmetry: WelfordStats::new(),
|
||||
stability_index: WelfordStats::new(),
|
||||
breathing_regularity: WelfordStats::new(),
|
||||
micro_tremor: WelfordStats::new(),
|
||||
activity_level: WelfordStats::new(),
|
||||
embedding_centroid: vec![0.0; embedding_dim],
|
||||
observation_days: 0,
|
||||
updated_at_us: 0,
|
||||
drift_counters: [0; 5],
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the Welford stats for a specific metric.
|
||||
pub fn stats_for(&self, metric: DriftMetric) -> &WelfordStats {
|
||||
match metric {
|
||||
DriftMetric::GaitSymmetry => &self.gait_symmetry,
|
||||
DriftMetric::StabilityIndex => &self.stability_index,
|
||||
DriftMetric::BreathingRegularity => &self.breathing_regularity,
|
||||
DriftMetric::MicroTremor => &self.micro_tremor,
|
||||
DriftMetric::ActivityLevel => &self.activity_level,
|
||||
}
|
||||
}
|
||||
|
||||
/// Get mutable Welford stats for a specific metric.
|
||||
fn stats_for_mut(&mut self, metric: DriftMetric) -> &mut WelfordStats {
|
||||
match metric {
|
||||
DriftMetric::GaitSymmetry => &mut self.gait_symmetry,
|
||||
DriftMetric::StabilityIndex => &mut self.stability_index,
|
||||
DriftMetric::BreathingRegularity => &mut self.breathing_regularity,
|
||||
DriftMetric::MicroTremor => &mut self.micro_tremor,
|
||||
DriftMetric::ActivityLevel => &mut self.activity_level,
|
||||
}
|
||||
}
|
||||
|
||||
/// Index of a metric in the drift_counters array.
|
||||
fn metric_index(metric: DriftMetric) -> usize {
|
||||
match metric {
|
||||
DriftMetric::GaitSymmetry => 0,
|
||||
DriftMetric::StabilityIndex => 1,
|
||||
DriftMetric::BreathingRegularity => 2,
|
||||
DriftMetric::MicroTremor => 3,
|
||||
DriftMetric::ActivityLevel => 4,
|
||||
}
|
||||
}
|
||||
|
||||
/// Whether baseline has enough data for drift detection.
|
||||
pub fn is_ready(&self) -> bool {
|
||||
self.observation_days >= 7
|
||||
}
|
||||
|
||||
/// Update baseline with a daily summary.
|
||||
///
|
||||
/// Returns drift reports for any metrics that exceed thresholds.
|
||||
pub fn update_daily(
|
||||
&mut self,
|
||||
summary: &DailyMetricSummary,
|
||||
timestamp_us: u64,
|
||||
) -> Vec<DriftReport> {
|
||||
self.observation_days += 1;
|
||||
self.updated_at_us = timestamp_us;
|
||||
|
||||
// Update embedding centroid with EMA (decay = 0.95)
|
||||
if let Some(ref emb) = summary.embedding_centroid {
|
||||
if emb.len() == self.embedding_centroid.len() {
|
||||
let alpha = 0.05_f32; // 1 - 0.95
|
||||
for (c, e) in self.embedding_centroid.iter_mut().zip(emb.iter()) {
|
||||
*c = (1.0 - alpha) * *c + alpha * *e;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let mut reports = Vec::new();
|
||||
|
||||
for &(metric, value) in &summary.metrics {
|
||||
let stats = self.stats_for_mut(metric);
|
||||
stats.update(value);
|
||||
|
||||
if !self.is_ready_at(self.observation_days) {
|
||||
continue;
|
||||
}
|
||||
|
||||
let z = stats.z_score(value);
|
||||
let idx = Self::metric_index(metric);
|
||||
|
||||
if z.abs() > 2.0 {
|
||||
self.drift_counters[idx] += 1;
|
||||
} else {
|
||||
self.drift_counters[idx] = 0;
|
||||
}
|
||||
|
||||
if self.drift_counters[idx] >= 3 {
|
||||
let direction = if z > 0.0 {
|
||||
DriftDirection::Increasing
|
||||
} else {
|
||||
DriftDirection::Decreasing
|
||||
};
|
||||
|
||||
let level = if self.drift_counters[idx] >= 7 {
|
||||
MonitoringLevel::RiskCorrelation
|
||||
} else {
|
||||
MonitoringLevel::Drift
|
||||
};
|
||||
|
||||
reports.push(DriftReport {
|
||||
person_id: self.person_id,
|
||||
metric,
|
||||
direction,
|
||||
z_score: z,
|
||||
current_value: value,
|
||||
baseline_mean: stats.mean,
|
||||
baseline_std: stats.std_dev(),
|
||||
sustained_days: self.drift_counters[idx],
|
||||
level,
|
||||
timestamp_us,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
reports
|
||||
}
|
||||
|
||||
/// Check readiness at a specific observation day count (internal helper).
|
||||
fn is_ready_at(&self, days: u32) -> bool {
|
||||
days >= 7
|
||||
}
|
||||
|
||||
/// Get current drift counter for a metric.
|
||||
pub fn drift_days(&self, metric: DriftMetric) -> u32 {
|
||||
self.drift_counters[Self::metric_index(metric)]
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Embedding history (simplified HNSW-indexed store)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Entry in the embedding history.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct EmbeddingEntry {
|
||||
/// Person ID.
|
||||
pub person_id: u64,
|
||||
/// Day timestamp (microseconds).
|
||||
pub day_us: u64,
|
||||
/// AETHER embedding vector.
|
||||
pub embedding: Vec<f32>,
|
||||
}
|
||||
|
||||
/// Simplified embedding history store for longitudinal tracking.
|
||||
///
|
||||
/// In production, this would be backed by an HNSW index for fast
|
||||
/// nearest-neighbor search. This implementation uses brute-force
|
||||
/// cosine similarity for correctness.
|
||||
#[derive(Debug)]
|
||||
pub struct EmbeddingHistory {
|
||||
entries: Vec<EmbeddingEntry>,
|
||||
max_entries: usize,
|
||||
embedding_dim: usize,
|
||||
}
|
||||
|
||||
impl EmbeddingHistory {
|
||||
/// Create a new embedding history store.
|
||||
pub fn new(embedding_dim: usize, max_entries: usize) -> Self {
|
||||
Self {
|
||||
entries: Vec::new(),
|
||||
max_entries,
|
||||
embedding_dim,
|
||||
}
|
||||
}
|
||||
|
||||
/// Add an embedding entry.
|
||||
pub fn push(&mut self, entry: EmbeddingEntry) -> Result<(), LongitudinalError> {
|
||||
if entry.embedding.len() != self.embedding_dim {
|
||||
return Err(LongitudinalError::EmbeddingDimensionMismatch {
|
||||
expected: self.embedding_dim,
|
||||
got: entry.embedding.len(),
|
||||
});
|
||||
}
|
||||
if self.entries.len() >= self.max_entries {
|
||||
self.entries.drain(..1); // FIFO eviction — acceptable for daily-rate inserts
|
||||
}
|
||||
self.entries.push(entry);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Find the K nearest embeddings to a query vector (brute-force cosine).
|
||||
pub fn search(&self, query: &[f32], k: usize) -> Vec<(usize, f32)> {
|
||||
let mut similarities: Vec<(usize, f32)> = self
|
||||
.entries
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, e)| (i, cosine_similarity(query, &e.embedding)))
|
||||
.collect();
|
||||
|
||||
similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
similarities.truncate(k);
|
||||
similarities
|
||||
}
|
||||
|
||||
/// Number of entries stored.
|
||||
pub fn len(&self) -> usize {
|
||||
self.entries.len()
|
||||
}
|
||||
|
||||
/// Whether the store is empty.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.entries.is_empty()
|
||||
}
|
||||
|
||||
/// Get entry by index.
|
||||
pub fn get(&self, index: usize) -> Option<&EmbeddingEntry> {
|
||||
self.entries.get(index)
|
||||
}
|
||||
|
||||
/// Get all entries for a specific person.
|
||||
pub fn entries_for_person(&self, person_id: u64) -> Vec<&EmbeddingEntry> {
|
||||
self.entries
|
||||
.iter()
|
||||
.filter(|e| e.person_id == person_id)
|
||||
.collect()
|
||||
}
|
||||
}
|
||||
|
||||
/// Cosine similarity between two f32 vectors.
|
||||
fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
|
||||
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
|
||||
let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
let denom = norm_a * norm_b;
|
||||
if denom < 1e-9 {
|
||||
0.0
|
||||
} else {
|
||||
dot / denom
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_daily_summary(person_id: u64, day: u64, values: [f64; 5]) -> DailyMetricSummary {
|
||||
DailyMetricSummary {
|
||||
person_id,
|
||||
day_us: day * 86_400_000_000,
|
||||
metrics: vec![
|
||||
(DriftMetric::GaitSymmetry, values[0]),
|
||||
(DriftMetric::StabilityIndex, values[1]),
|
||||
(DriftMetric::BreathingRegularity, values[2]),
|
||||
(DriftMetric::MicroTremor, values[3]),
|
||||
(DriftMetric::ActivityLevel, values[4]),
|
||||
],
|
||||
embedding_centroid: None,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_personal_baseline_creation() {
|
||||
let baseline = PersonalBaseline::new(42, 128);
|
||||
assert_eq!(baseline.person_id, 42);
|
||||
assert_eq!(baseline.observation_days, 0);
|
||||
assert!(!baseline.is_ready());
|
||||
assert_eq!(baseline.embedding_centroid.len(), 128);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_baseline_not_ready_before_7_days() {
|
||||
let mut baseline = PersonalBaseline::new(1, 128);
|
||||
for day in 0..6 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
let reports = baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
assert!(reports.is_empty(), "No drift before 7 days");
|
||||
}
|
||||
assert!(!baseline.is_ready());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_baseline_ready_after_7_days() {
|
||||
let mut baseline = PersonalBaseline::new(1, 128);
|
||||
for day in 0..7 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
}
|
||||
assert!(baseline.is_ready());
|
||||
assert_eq!(baseline.observation_days, 7);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stable_metrics_no_drift() {
|
||||
let mut baseline = PersonalBaseline::new(1, 128);
|
||||
|
||||
// 20 days of stable metrics
|
||||
for day in 0..20 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
let reports = baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
assert!(
|
||||
reports.is_empty(),
|
||||
"Stable metrics should not trigger drift"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_drift_detected_after_sustained_deviation() {
|
||||
let mut baseline = PersonalBaseline::new(1, 128);
|
||||
|
||||
// 10 days of stable gait symmetry = 0.1
|
||||
for day in 0..10 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
}
|
||||
|
||||
// Now inject large drift in gait symmetry for 3+ days
|
||||
let mut any_drift = false;
|
||||
for day in 10..16 {
|
||||
let summary = make_daily_summary(1, day, [0.9, 0.9, 0.15, 0.5, 0.7]);
|
||||
let reports = baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
if !reports.is_empty() {
|
||||
any_drift = true;
|
||||
let r = &reports[0];
|
||||
assert_eq!(r.metric, DriftMetric::GaitSymmetry);
|
||||
assert_eq!(r.direction, DriftDirection::Increasing);
|
||||
assert!(r.z_score > 2.0);
|
||||
assert!(r.sustained_days >= 3);
|
||||
}
|
||||
}
|
||||
assert!(any_drift, "Should detect drift after sustained deviation");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_drift_resolves_when_metric_returns() {
|
||||
let mut baseline = PersonalBaseline::new(1, 128);
|
||||
|
||||
// Stable baseline
|
||||
for day in 0..10 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
}
|
||||
|
||||
// Drift for 3 days
|
||||
for day in 10..13 {
|
||||
let summary = make_daily_summary(1, day, [0.9, 0.9, 0.15, 0.5, 0.7]);
|
||||
baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
}
|
||||
|
||||
// Return to normal
|
||||
for day in 13..16 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
let reports = baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
// After returning to normal, drift counter resets
|
||||
if day == 15 {
|
||||
assert!(reports.is_empty(), "Drift should resolve");
|
||||
assert_eq!(baseline.drift_days(DriftMetric::GaitSymmetry), 0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_monitoring_level_escalation() {
|
||||
let mut baseline = PersonalBaseline::new(1, 128);
|
||||
|
||||
for day in 0..10 {
|
||||
let summary = make_daily_summary(1, day, [0.1, 0.9, 0.15, 0.5, 0.7]);
|
||||
baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
}
|
||||
|
||||
// Sustained drift for 7+ days should escalate to RiskCorrelation
|
||||
let mut max_level = MonitoringLevel::Physiological;
|
||||
for day in 10..20 {
|
||||
let summary = make_daily_summary(1, day, [0.9, 0.9, 0.15, 0.5, 0.7]);
|
||||
let reports = baseline.update_daily(&summary, day * 86_400_000_000);
|
||||
for r in &reports {
|
||||
if r.level > max_level {
|
||||
max_level = r.level;
|
||||
}
|
||||
}
|
||||
}
|
||||
assert_eq!(
|
||||
max_level,
|
||||
MonitoringLevel::RiskCorrelation,
|
||||
"7+ days sustained drift should reach RiskCorrelation level"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_history_push_and_search() {
|
||||
let mut history = EmbeddingHistory::new(4, 100);
|
||||
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 1,
|
||||
day_us: 0,
|
||||
embedding: vec![1.0, 0.0, 0.0, 0.0],
|
||||
})
|
||||
.unwrap();
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 1,
|
||||
day_us: 1,
|
||||
embedding: vec![0.9, 0.1, 0.0, 0.0],
|
||||
})
|
||||
.unwrap();
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 2,
|
||||
day_us: 0,
|
||||
embedding: vec![0.0, 0.0, 1.0, 0.0],
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
let results = history.search(&[1.0, 0.0, 0.0, 0.0], 2);
|
||||
assert_eq!(results.len(), 2);
|
||||
// First result should be exact match
|
||||
assert!((results[0].1 - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_history_dimension_mismatch() {
|
||||
let mut history = EmbeddingHistory::new(4, 100);
|
||||
let result = history.push(EmbeddingEntry {
|
||||
person_id: 1,
|
||||
day_us: 0,
|
||||
embedding: vec![1.0, 0.0], // wrong dim
|
||||
});
|
||||
assert!(matches!(
|
||||
result,
|
||||
Err(LongitudinalError::EmbeddingDimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_history_fifo_eviction() {
|
||||
let mut history = EmbeddingHistory::new(2, 3);
|
||||
for i in 0..5 {
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 1,
|
||||
day_us: i,
|
||||
embedding: vec![i as f32, 0.0],
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
assert_eq!(history.len(), 3);
|
||||
// First entry should be day 2 (0 and 1 evicted)
|
||||
assert_eq!(history.get(0).unwrap().day_us, 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_entries_for_person() {
|
||||
let mut history = EmbeddingHistory::new(2, 100);
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 1,
|
||||
day_us: 0,
|
||||
embedding: vec![1.0, 0.0],
|
||||
})
|
||||
.unwrap();
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 2,
|
||||
day_us: 0,
|
||||
embedding: vec![0.0, 1.0],
|
||||
})
|
||||
.unwrap();
|
||||
history
|
||||
.push(EmbeddingEntry {
|
||||
person_id: 1,
|
||||
day_us: 1,
|
||||
embedding: vec![0.9, 0.1],
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
let entries = history.entries_for_person(1);
|
||||
assert_eq!(entries.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_drift_metric_names() {
|
||||
assert_eq!(DriftMetric::GaitSymmetry.name(), "gait_symmetry");
|
||||
assert_eq!(DriftMetric::ActivityLevel.name(), "activity_level");
|
||||
assert_eq!(DriftMetric::all().len(), 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_unit_vectors() {
|
||||
let a = vec![1.0_f32, 0.0, 0.0];
|
||||
let b = vec![0.0_f32, 1.0, 0.0];
|
||||
assert!(cosine_similarity(&a, &b).abs() < 1e-6, "Orthogonal = 0");
|
||||
|
||||
let c = vec![1.0_f32, 0.0, 0.0];
|
||||
assert!((cosine_similarity(&a, &c) - 1.0).abs() < 1e-6, "Same = 1");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,320 @@
|
||||
//! RuvSense -- Sensing-First RF Mode for Multistatic WiFi DensePose (ADR-029)
|
||||
//!
|
||||
//! This bounded context implements the multistatic sensing pipeline that fuses
|
||||
//! CSI from multiple ESP32 nodes across multiple WiFi channels into a single
|
||||
//! coherent sensing frame per 50 ms TDMA cycle (20 Hz output).
|
||||
//!
|
||||
//! # Architecture
|
||||
//!
|
||||
//! The pipeline flows through six stages:
|
||||
//!
|
||||
//! 1. **Multi-Band Fusion** (`multiband`) -- Aggregate per-channel CSI frames
|
||||
//! from channel-hopping into a wideband virtual snapshot per node.
|
||||
//! 2. **Phase Alignment** (`phase_align`) -- Correct LO-induced phase rotation
|
||||
//! between channels using `ruvector-solver::NeumannSolver`.
|
||||
//! 3. **Multistatic Fusion** (`multistatic`) -- Fuse N node observations into
|
||||
//! a single `FusedSensingFrame` with attention-based cross-node weighting
|
||||
//! via `ruvector-attn-mincut`.
|
||||
//! 4. **Coherence Scoring** (`coherence`) -- Compute per-subcarrier z-score
|
||||
//! coherence against a rolling reference template.
|
||||
//! 5. **Coherence Gating** (`coherence_gate`) -- Apply threshold-based gate
|
||||
//! decision: Accept / PredictOnly / Reject / Recalibrate.
|
||||
//! 6. **Pose Tracking** (`pose_tracker`) -- 17-keypoint Kalman tracker with
|
||||
//! lifecycle state machine and AETHER re-ID embedding support.
|
||||
//!
|
||||
//! # RuVector Crate Usage
|
||||
//!
|
||||
//! - `ruvector-solver` -- Phase alignment, coherence decomposition
|
||||
//! - `ruvector-attn-mincut` -- Cross-node spectrogram fusion
|
||||
//! - `ruvector-mincut` -- Person separation and track assignment
|
||||
//! - `ruvector-attention` -- Cross-channel feature weighting
|
||||
//!
|
||||
//! # References
|
||||
//!
|
||||
//! - ADR-029: Project RuvSense
|
||||
//! - IEEE 802.11bf-2024 WLAN Sensing
|
||||
|
||||
// ADR-030: Exotic sensing tiers
|
||||
pub mod adversarial;
|
||||
pub mod cross_room;
|
||||
pub mod field_model;
|
||||
pub mod gesture;
|
||||
pub mod intention;
|
||||
pub mod longitudinal;
|
||||
pub mod tomography;
|
||||
|
||||
// ADR-029: Core multistatic pipeline
|
||||
pub mod coherence;
|
||||
pub mod coherence_gate;
|
||||
pub mod multiband;
|
||||
pub mod multistatic;
|
||||
pub mod phase_align;
|
||||
pub mod pose_tracker;
|
||||
|
||||
// Re-export core types for ergonomic access
|
||||
pub use coherence::CoherenceState;
|
||||
pub use coherence_gate::{GateDecision, GatePolicy};
|
||||
pub use multiband::MultiBandCsiFrame;
|
||||
pub use multistatic::FusedSensingFrame;
|
||||
pub use phase_align::{PhaseAligner, PhaseAlignError};
|
||||
pub use pose_tracker::{KeypointState, PoseTrack, TrackLifecycleState};
|
||||
|
||||
/// Number of keypoints in a full-body pose skeleton (COCO-17).
|
||||
pub const NUM_KEYPOINTS: usize = 17;
|
||||
|
||||
/// Keypoint indices following the COCO-17 convention.
|
||||
pub mod keypoint {
|
||||
pub const NOSE: usize = 0;
|
||||
pub const LEFT_EYE: usize = 1;
|
||||
pub const RIGHT_EYE: usize = 2;
|
||||
pub const LEFT_EAR: usize = 3;
|
||||
pub const RIGHT_EAR: usize = 4;
|
||||
pub const LEFT_SHOULDER: usize = 5;
|
||||
pub const RIGHT_SHOULDER: usize = 6;
|
||||
pub const LEFT_ELBOW: usize = 7;
|
||||
pub const RIGHT_ELBOW: usize = 8;
|
||||
pub const LEFT_WRIST: usize = 9;
|
||||
pub const RIGHT_WRIST: usize = 10;
|
||||
pub const LEFT_HIP: usize = 11;
|
||||
pub const RIGHT_HIP: usize = 12;
|
||||
pub const LEFT_KNEE: usize = 13;
|
||||
pub const RIGHT_KNEE: usize = 14;
|
||||
pub const LEFT_ANKLE: usize = 15;
|
||||
pub const RIGHT_ANKLE: usize = 16;
|
||||
|
||||
/// Torso keypoint indices (shoulders, hips, spine midpoint proxy).
|
||||
pub const TORSO_INDICES: &[usize] = &[
|
||||
LEFT_SHOULDER,
|
||||
RIGHT_SHOULDER,
|
||||
LEFT_HIP,
|
||||
RIGHT_HIP,
|
||||
];
|
||||
}
|
||||
|
||||
/// Unique identifier for a pose track.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
|
||||
pub struct TrackId(pub u64);
|
||||
|
||||
impl TrackId {
|
||||
/// Create a new track identifier.
|
||||
pub fn new(id: u64) -> Self {
|
||||
Self(id)
|
||||
}
|
||||
}
|
||||
|
||||
impl std::fmt::Display for TrackId {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
write!(f, "Track({})", self.0)
|
||||
}
|
||||
}
|
||||
|
||||
/// Error type shared across the RuvSense pipeline.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum RuvSenseError {
|
||||
/// Phase alignment failed.
|
||||
#[error("Phase alignment error: {0}")]
|
||||
PhaseAlign(#[from] phase_align::PhaseAlignError),
|
||||
|
||||
/// Multi-band fusion error.
|
||||
#[error("Multi-band fusion error: {0}")]
|
||||
MultiBand(#[from] multiband::MultiBandError),
|
||||
|
||||
/// Multistatic fusion error.
|
||||
#[error("Multistatic fusion error: {0}")]
|
||||
Multistatic(#[from] multistatic::MultistaticError),
|
||||
|
||||
/// Coherence computation error.
|
||||
#[error("Coherence error: {0}")]
|
||||
Coherence(#[from] coherence::CoherenceError),
|
||||
|
||||
/// Pose tracker error.
|
||||
#[error("Pose tracker error: {0}")]
|
||||
PoseTracker(#[from] pose_tracker::PoseTrackerError),
|
||||
}
|
||||
|
||||
/// Common result type for RuvSense operations.
|
||||
pub type Result<T> = std::result::Result<T, RuvSenseError>;
|
||||
|
||||
/// Configuration for the RuvSense pipeline.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RuvSenseConfig {
|
||||
/// Maximum number of nodes in the multistatic mesh.
|
||||
pub max_nodes: usize,
|
||||
/// Target output rate in Hz.
|
||||
pub target_hz: f64,
|
||||
/// Number of channels in the hop sequence.
|
||||
pub num_channels: usize,
|
||||
/// Coherence accept threshold (default 0.85).
|
||||
pub coherence_accept: f32,
|
||||
/// Coherence drift threshold (default 0.5).
|
||||
pub coherence_drift: f32,
|
||||
/// Maximum stale frames before recalibration (default 200 = 10s at 20Hz).
|
||||
pub max_stale_frames: u64,
|
||||
/// Embedding dimension for AETHER re-ID (default 128).
|
||||
pub embedding_dim: usize,
|
||||
}
|
||||
|
||||
impl Default for RuvSenseConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
max_nodes: 4,
|
||||
target_hz: 20.0,
|
||||
num_channels: 3,
|
||||
coherence_accept: 0.85,
|
||||
coherence_drift: 0.5,
|
||||
max_stale_frames: 200,
|
||||
embedding_dim: 128,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Top-level pipeline orchestrator for RuvSense multistatic sensing.
|
||||
///
|
||||
/// Coordinates the flow from raw per-node CSI frames through multi-band
|
||||
/// fusion, phase alignment, multistatic fusion, coherence gating, and
|
||||
/// finally into the pose tracker.
|
||||
pub struct RuvSensePipeline {
|
||||
config: RuvSenseConfig,
|
||||
phase_aligner: PhaseAligner,
|
||||
coherence_state: CoherenceState,
|
||||
gate_policy: GatePolicy,
|
||||
frame_counter: u64,
|
||||
}
|
||||
|
||||
impl RuvSensePipeline {
|
||||
/// Create a new pipeline with default configuration.
|
||||
pub fn new() -> Self {
|
||||
Self::with_config(RuvSenseConfig::default())
|
||||
}
|
||||
|
||||
/// Create a new pipeline with the given configuration.
|
||||
pub fn with_config(config: RuvSenseConfig) -> Self {
|
||||
let n_sub = 56; // canonical subcarrier count
|
||||
Self {
|
||||
phase_aligner: PhaseAligner::new(config.num_channels),
|
||||
coherence_state: CoherenceState::new(n_sub, config.coherence_accept),
|
||||
gate_policy: GatePolicy::new(
|
||||
config.coherence_accept,
|
||||
config.coherence_drift,
|
||||
config.max_stale_frames,
|
||||
),
|
||||
config,
|
||||
frame_counter: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Return a reference to the current pipeline configuration.
|
||||
pub fn config(&self) -> &RuvSenseConfig {
|
||||
&self.config
|
||||
}
|
||||
|
||||
/// Return the total number of frames processed.
|
||||
pub fn frame_count(&self) -> u64 {
|
||||
self.frame_counter
|
||||
}
|
||||
|
||||
/// Return a reference to the current coherence state.
|
||||
pub fn coherence_state(&self) -> &CoherenceState {
|
||||
&self.coherence_state
|
||||
}
|
||||
|
||||
/// Advance the frame counter (called once per sensing cycle).
|
||||
pub fn tick(&mut self) {
|
||||
self.frame_counter += 1;
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for RuvSensePipeline {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn default_config_values() {
|
||||
let cfg = RuvSenseConfig::default();
|
||||
assert_eq!(cfg.max_nodes, 4);
|
||||
assert!((cfg.target_hz - 20.0).abs() < f64::EPSILON);
|
||||
assert_eq!(cfg.num_channels, 3);
|
||||
assert!((cfg.coherence_accept - 0.85).abs() < f32::EPSILON);
|
||||
assert!((cfg.coherence_drift - 0.5).abs() < f32::EPSILON);
|
||||
assert_eq!(cfg.max_stale_frames, 200);
|
||||
assert_eq!(cfg.embedding_dim, 128);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pipeline_creation_defaults() {
|
||||
let pipe = RuvSensePipeline::new();
|
||||
assert_eq!(pipe.frame_count(), 0);
|
||||
assert_eq!(pipe.config().max_nodes, 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pipeline_tick_increments() {
|
||||
let mut pipe = RuvSensePipeline::new();
|
||||
pipe.tick();
|
||||
pipe.tick();
|
||||
pipe.tick();
|
||||
assert_eq!(pipe.frame_count(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_id_display() {
|
||||
let tid = TrackId::new(42);
|
||||
assert_eq!(format!("{}", tid), "Track(42)");
|
||||
assert_eq!(tid.0, 42);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_id_equality() {
|
||||
assert_eq!(TrackId(1), TrackId(1));
|
||||
assert_ne!(TrackId(1), TrackId(2));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn keypoint_constants() {
|
||||
assert_eq!(keypoint::NOSE, 0);
|
||||
assert_eq!(keypoint::LEFT_ANKLE, 15);
|
||||
assert_eq!(keypoint::RIGHT_ANKLE, 16);
|
||||
assert_eq!(keypoint::TORSO_INDICES.len(), 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn num_keypoints_is_17() {
|
||||
assert_eq!(NUM_KEYPOINTS, 17);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn custom_config_pipeline() {
|
||||
let cfg = RuvSenseConfig {
|
||||
max_nodes: 6,
|
||||
target_hz: 10.0,
|
||||
num_channels: 6,
|
||||
coherence_accept: 0.9,
|
||||
coherence_drift: 0.4,
|
||||
max_stale_frames: 100,
|
||||
embedding_dim: 64,
|
||||
};
|
||||
let pipe = RuvSensePipeline::with_config(cfg);
|
||||
assert_eq!(pipe.config().max_nodes, 6);
|
||||
assert!((pipe.config().target_hz - 10.0).abs() < f64::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn error_display() {
|
||||
let err = RuvSenseError::Coherence(coherence::CoherenceError::EmptyInput);
|
||||
let msg = format!("{}", err);
|
||||
assert!(msg.contains("Coherence"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pipeline_coherence_state_accessible() {
|
||||
let pipe = RuvSensePipeline::new();
|
||||
let cs = pipe.coherence_state();
|
||||
assert!(cs.score() >= 0.0);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,441 @@
|
||||
//! Multi-Band CSI Frame Fusion (ADR-029 Section 2.3)
|
||||
//!
|
||||
//! Aggregates per-channel CSI frames from channel-hopping into a wideband
|
||||
//! virtual snapshot. An ESP32-S3 cycling through channels 1/6/11 at 50 ms
|
||||
//! dwell per channel yields 3 canonical-56 CSI rows per sensing cycle.
|
||||
//! This module fuses them into a single `MultiBandCsiFrame` annotated with
|
||||
//! center frequencies and cross-channel coherence.
|
||||
//!
|
||||
//! # RuVector Integration
|
||||
//!
|
||||
//! - `ruvector-attention` for cross-channel feature weighting (future)
|
||||
|
||||
use crate::hardware_norm::CanonicalCsiFrame;
|
||||
|
||||
/// Errors from multi-band frame fusion.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum MultiBandError {
|
||||
/// No channel frames provided.
|
||||
#[error("No channel frames provided for multi-band fusion")]
|
||||
NoFrames,
|
||||
|
||||
/// Mismatched subcarrier counts across channels.
|
||||
#[error("Subcarrier count mismatch: channel {channel_idx} has {got}, expected {expected}")]
|
||||
SubcarrierMismatch {
|
||||
channel_idx: usize,
|
||||
expected: usize,
|
||||
got: usize,
|
||||
},
|
||||
|
||||
/// Frequency list length does not match frame count.
|
||||
#[error("Frequency count ({freq_count}) does not match frame count ({frame_count})")]
|
||||
FrequencyCountMismatch { freq_count: usize, frame_count: usize },
|
||||
|
||||
/// Duplicate frequency in channel list.
|
||||
#[error("Duplicate frequency {freq_mhz} MHz at index {idx}")]
|
||||
DuplicateFrequency { freq_mhz: u32, idx: usize },
|
||||
}
|
||||
|
||||
/// Fused multi-band CSI from one node at one time slot.
|
||||
///
|
||||
/// Holds one canonical-56 row per channel, ordered by center frequency.
|
||||
/// The `coherence` field quantifies agreement across channels (0.0-1.0).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct MultiBandCsiFrame {
|
||||
/// Originating node identifier (0-255).
|
||||
pub node_id: u8,
|
||||
/// Timestamp of the sensing cycle in microseconds.
|
||||
pub timestamp_us: u64,
|
||||
/// One canonical-56 CSI frame per channel, ordered by center frequency.
|
||||
pub channel_frames: Vec<CanonicalCsiFrame>,
|
||||
/// Center frequencies (MHz) for each channel row.
|
||||
pub frequencies_mhz: Vec<u32>,
|
||||
/// Cross-channel coherence score (0.0-1.0).
|
||||
pub coherence: f32,
|
||||
}
|
||||
|
||||
/// Configuration for the multi-band fusion process.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct MultiBandConfig {
|
||||
/// Time window in microseconds within which frames are considered
|
||||
/// part of the same sensing cycle.
|
||||
pub window_us: u64,
|
||||
/// Expected number of channels per cycle.
|
||||
pub expected_channels: usize,
|
||||
/// Minimum coherence to accept the fused frame.
|
||||
pub min_coherence: f32,
|
||||
}
|
||||
|
||||
impl Default for MultiBandConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
window_us: 200_000, // 200 ms default window
|
||||
expected_channels: 3,
|
||||
min_coherence: 0.3,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Builder for constructing a `MultiBandCsiFrame` from per-channel observations.
|
||||
#[derive(Debug)]
|
||||
pub struct MultiBandBuilder {
|
||||
node_id: u8,
|
||||
timestamp_us: u64,
|
||||
frames: Vec<CanonicalCsiFrame>,
|
||||
frequencies: Vec<u32>,
|
||||
}
|
||||
|
||||
impl MultiBandBuilder {
|
||||
/// Create a new builder for the given node and timestamp.
|
||||
pub fn new(node_id: u8, timestamp_us: u64) -> Self {
|
||||
Self {
|
||||
node_id,
|
||||
timestamp_us,
|
||||
frames: Vec::new(),
|
||||
frequencies: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Add a channel observation at the given center frequency.
|
||||
pub fn add_channel(
|
||||
mut self,
|
||||
frame: CanonicalCsiFrame,
|
||||
freq_mhz: u32,
|
||||
) -> Self {
|
||||
self.frames.push(frame);
|
||||
self.frequencies.push(freq_mhz);
|
||||
self
|
||||
}
|
||||
|
||||
/// Build the fused multi-band frame.
|
||||
///
|
||||
/// Validates inputs, sorts by frequency, and computes cross-channel coherence.
|
||||
pub fn build(mut self) -> std::result::Result<MultiBandCsiFrame, MultiBandError> {
|
||||
if self.frames.is_empty() {
|
||||
return Err(MultiBandError::NoFrames);
|
||||
}
|
||||
|
||||
if self.frequencies.len() != self.frames.len() {
|
||||
return Err(MultiBandError::FrequencyCountMismatch {
|
||||
freq_count: self.frequencies.len(),
|
||||
frame_count: self.frames.len(),
|
||||
});
|
||||
}
|
||||
|
||||
// Check for duplicate frequencies
|
||||
for i in 0..self.frequencies.len() {
|
||||
for j in (i + 1)..self.frequencies.len() {
|
||||
if self.frequencies[i] == self.frequencies[j] {
|
||||
return Err(MultiBandError::DuplicateFrequency {
|
||||
freq_mhz: self.frequencies[i],
|
||||
idx: j,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Validate consistent subcarrier counts
|
||||
let expected_len = self.frames[0].amplitude.len();
|
||||
for (i, frame) in self.frames.iter().enumerate().skip(1) {
|
||||
if frame.amplitude.len() != expected_len {
|
||||
return Err(MultiBandError::SubcarrierMismatch {
|
||||
channel_idx: i,
|
||||
expected: expected_len,
|
||||
got: frame.amplitude.len(),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Sort frames by frequency
|
||||
let mut indices: Vec<usize> = (0..self.frames.len()).collect();
|
||||
indices.sort_by_key(|&i| self.frequencies[i]);
|
||||
|
||||
let sorted_frames: Vec<CanonicalCsiFrame> =
|
||||
indices.iter().map(|&i| self.frames[i].clone()).collect();
|
||||
let sorted_freqs: Vec<u32> =
|
||||
indices.iter().map(|&i| self.frequencies[i]).collect();
|
||||
|
||||
self.frames = sorted_frames;
|
||||
self.frequencies = sorted_freqs;
|
||||
|
||||
// Compute cross-channel coherence
|
||||
let coherence = compute_cross_channel_coherence(&self.frames);
|
||||
|
||||
Ok(MultiBandCsiFrame {
|
||||
node_id: self.node_id,
|
||||
timestamp_us: self.timestamp_us,
|
||||
channel_frames: self.frames,
|
||||
frequencies_mhz: self.frequencies,
|
||||
coherence,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute cross-channel coherence as the mean pairwise Pearson correlation
|
||||
/// of amplitude vectors across all channel pairs.
|
||||
///
|
||||
/// Returns a value in [0.0, 1.0] where 1.0 means perfect correlation.
|
||||
fn compute_cross_channel_coherence(frames: &[CanonicalCsiFrame]) -> f32 {
|
||||
if frames.len() < 2 {
|
||||
return 1.0; // single channel is trivially coherent
|
||||
}
|
||||
|
||||
let mut total_corr = 0.0_f64;
|
||||
let mut pair_count = 0u32;
|
||||
|
||||
for i in 0..frames.len() {
|
||||
for j in (i + 1)..frames.len() {
|
||||
let corr = pearson_correlation_f32(
|
||||
&frames[i].amplitude,
|
||||
&frames[j].amplitude,
|
||||
);
|
||||
total_corr += corr as f64;
|
||||
pair_count += 1;
|
||||
}
|
||||
}
|
||||
|
||||
if pair_count == 0 {
|
||||
return 1.0;
|
||||
}
|
||||
|
||||
// Map correlation [-1, 1] to coherence [0, 1]
|
||||
let mean_corr = total_corr / pair_count as f64;
|
||||
((mean_corr + 1.0) / 2.0).clamp(0.0, 1.0) as f32
|
||||
}
|
||||
|
||||
/// Pearson correlation coefficient between two f32 slices.
|
||||
fn pearson_correlation_f32(a: &[f32], b: &[f32]) -> f32 {
|
||||
let n = a.len().min(b.len());
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let n_f = n as f32;
|
||||
let mean_a: f32 = a[..n].iter().sum::<f32>() / n_f;
|
||||
let mean_b: f32 = b[..n].iter().sum::<f32>() / n_f;
|
||||
|
||||
let mut cov = 0.0_f32;
|
||||
let mut var_a = 0.0_f32;
|
||||
let mut var_b = 0.0_f32;
|
||||
|
||||
for i in 0..n {
|
||||
let da = a[i] - mean_a;
|
||||
let db = b[i] - mean_b;
|
||||
cov += da * db;
|
||||
var_a += da * da;
|
||||
var_b += db * db;
|
||||
}
|
||||
|
||||
let denom = (var_a * var_b).sqrt();
|
||||
if denom < 1e-12 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
(cov / denom).clamp(-1.0, 1.0)
|
||||
}
|
||||
|
||||
/// Concatenate the amplitude vectors from all channels into a single
|
||||
/// wideband amplitude vector. Useful for downstream models that expect
|
||||
/// a flat feature vector.
|
||||
pub fn concatenate_amplitudes(frame: &MultiBandCsiFrame) -> Vec<f32> {
|
||||
let total_len: usize = frame.channel_frames.iter().map(|f| f.amplitude.len()).sum();
|
||||
let mut out = Vec::with_capacity(total_len);
|
||||
for cf in &frame.channel_frames {
|
||||
out.extend_from_slice(&cf.amplitude);
|
||||
}
|
||||
out
|
||||
}
|
||||
|
||||
/// Compute the mean amplitude across all channels, producing a single
|
||||
/// canonical-length vector that averages multi-band observations.
|
||||
pub fn mean_amplitude(frame: &MultiBandCsiFrame) -> Vec<f32> {
|
||||
if frame.channel_frames.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let n_sub = frame.channel_frames[0].amplitude.len();
|
||||
let n_ch = frame.channel_frames.len() as f32;
|
||||
let mut mean = vec![0.0_f32; n_sub];
|
||||
|
||||
for cf in &frame.channel_frames {
|
||||
for (i, &val) in cf.amplitude.iter().enumerate() {
|
||||
if i < n_sub {
|
||||
mean[i] += val;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for v in &mut mean {
|
||||
*v /= n_ch;
|
||||
}
|
||||
|
||||
mean
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::hardware_norm::HardwareType;
|
||||
|
||||
fn make_canonical(amplitude: Vec<f32>, phase: Vec<f32>) -> CanonicalCsiFrame {
|
||||
CanonicalCsiFrame {
|
||||
amplitude,
|
||||
phase,
|
||||
hardware_type: HardwareType::Esp32S3,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_frame(n_sub: usize, scale: f32) -> CanonicalCsiFrame {
|
||||
let amp: Vec<f32> = (0..n_sub).map(|i| scale * (i as f32 * 0.1).sin()).collect();
|
||||
let phase: Vec<f32> = (0..n_sub).map(|i| (i as f32 * 0.05).cos()).collect();
|
||||
make_canonical(amp, phase)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn build_single_channel() {
|
||||
let frame = MultiBandBuilder::new(0, 1000)
|
||||
.add_channel(make_frame(56, 1.0), 2412)
|
||||
.build()
|
||||
.unwrap();
|
||||
assert_eq!(frame.node_id, 0);
|
||||
assert_eq!(frame.timestamp_us, 1000);
|
||||
assert_eq!(frame.channel_frames.len(), 1);
|
||||
assert_eq!(frame.frequencies_mhz, vec![2412]);
|
||||
assert!((frame.coherence - 1.0).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn build_three_channels_sorted_by_freq() {
|
||||
let frame = MultiBandBuilder::new(1, 2000)
|
||||
.add_channel(make_frame(56, 1.0), 2462) // ch 11
|
||||
.add_channel(make_frame(56, 1.0), 2412) // ch 1
|
||||
.add_channel(make_frame(56, 1.0), 2437) // ch 6
|
||||
.build()
|
||||
.unwrap();
|
||||
assert_eq!(frame.frequencies_mhz, vec![2412, 2437, 2462]);
|
||||
assert_eq!(frame.channel_frames.len(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_frames_error() {
|
||||
let result = MultiBandBuilder::new(0, 0).build();
|
||||
assert!(matches!(result, Err(MultiBandError::NoFrames)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn subcarrier_mismatch_error() {
|
||||
let result = MultiBandBuilder::new(0, 0)
|
||||
.add_channel(make_frame(56, 1.0), 2412)
|
||||
.add_channel(make_frame(30, 1.0), 2437)
|
||||
.build();
|
||||
assert!(matches!(result, Err(MultiBandError::SubcarrierMismatch { .. })));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn duplicate_frequency_error() {
|
||||
let result = MultiBandBuilder::new(0, 0)
|
||||
.add_channel(make_frame(56, 1.0), 2412)
|
||||
.add_channel(make_frame(56, 1.0), 2412)
|
||||
.build();
|
||||
assert!(matches!(result, Err(MultiBandError::DuplicateFrequency { .. })));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_identical_channels() {
|
||||
let f = make_frame(56, 1.0);
|
||||
let frame = MultiBandBuilder::new(0, 0)
|
||||
.add_channel(f.clone(), 2412)
|
||||
.add_channel(f.clone(), 2437)
|
||||
.build()
|
||||
.unwrap();
|
||||
// Identical channels should have coherence == 1.0
|
||||
assert!((frame.coherence - 1.0).abs() < 0.01);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_orthogonal_channels() {
|
||||
let n = 56;
|
||||
let amp_a: Vec<f32> = (0..n).map(|i| (i as f32 * 0.3).sin()).collect();
|
||||
let amp_b: Vec<f32> = (0..n).map(|i| (i as f32 * 0.3).cos()).collect();
|
||||
let ph = vec![0.0_f32; n];
|
||||
|
||||
let frame = MultiBandBuilder::new(0, 0)
|
||||
.add_channel(make_canonical(amp_a, ph.clone()), 2412)
|
||||
.add_channel(make_canonical(amp_b, ph), 2437)
|
||||
.build()
|
||||
.unwrap();
|
||||
// Orthogonal signals should produce lower coherence
|
||||
assert!(frame.coherence < 0.9);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn concatenate_amplitudes_correct_length() {
|
||||
let frame = MultiBandBuilder::new(0, 0)
|
||||
.add_channel(make_frame(56, 1.0), 2412)
|
||||
.add_channel(make_frame(56, 2.0), 2437)
|
||||
.add_channel(make_frame(56, 3.0), 2462)
|
||||
.build()
|
||||
.unwrap();
|
||||
let concat = concatenate_amplitudes(&frame);
|
||||
assert_eq!(concat.len(), 56 * 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mean_amplitude_correct() {
|
||||
let n = 4;
|
||||
let f1 = make_canonical(vec![1.0, 2.0, 3.0, 4.0], vec![0.0; n]);
|
||||
let f2 = make_canonical(vec![3.0, 4.0, 5.0, 6.0], vec![0.0; n]);
|
||||
let frame = MultiBandBuilder::new(0, 0)
|
||||
.add_channel(f1, 2412)
|
||||
.add_channel(f2, 2437)
|
||||
.build()
|
||||
.unwrap();
|
||||
let m = mean_amplitude(&frame);
|
||||
assert_eq!(m.len(), 4);
|
||||
assert!((m[0] - 2.0).abs() < 1e-6);
|
||||
assert!((m[1] - 3.0).abs() < 1e-6);
|
||||
assert!((m[2] - 4.0).abs() < 1e-6);
|
||||
assert!((m[3] - 5.0).abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mean_amplitude_empty() {
|
||||
let frame = MultiBandCsiFrame {
|
||||
node_id: 0,
|
||||
timestamp_us: 0,
|
||||
channel_frames: vec![],
|
||||
frequencies_mhz: vec![],
|
||||
coherence: 1.0,
|
||||
};
|
||||
assert!(mean_amplitude(&frame).is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pearson_correlation_perfect() {
|
||||
let a = vec![1.0_f32, 2.0, 3.0, 4.0, 5.0];
|
||||
let b = vec![2.0_f32, 4.0, 6.0, 8.0, 10.0];
|
||||
let r = pearson_correlation_f32(&a, &b);
|
||||
assert!((r - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pearson_correlation_negative() {
|
||||
let a = vec![1.0_f32, 2.0, 3.0, 4.0, 5.0];
|
||||
let b = vec![5.0_f32, 4.0, 3.0, 2.0, 1.0];
|
||||
let r = pearson_correlation_f32(&a, &b);
|
||||
assert!((r + 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pearson_correlation_empty() {
|
||||
assert_eq!(pearson_correlation_f32(&[], &[]), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn default_config() {
|
||||
let cfg = MultiBandConfig::default();
|
||||
assert_eq!(cfg.expected_channels, 3);
|
||||
assert_eq!(cfg.window_us, 200_000);
|
||||
assert!((cfg.min_coherence - 0.3).abs() < f32::EPSILON);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,557 @@
|
||||
//! Multistatic Viewpoint Fusion (ADR-029 Section 2.4)
|
||||
//!
|
||||
//! With N ESP32 nodes in a TDMA mesh, each sensing cycle produces N
|
||||
//! `MultiBandCsiFrame`s. This module fuses them into a single
|
||||
//! `FusedSensingFrame` using attention-based cross-node weighting.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//!
|
||||
//! 1. Collect N `MultiBandCsiFrame`s from the current sensing cycle.
|
||||
//! 2. Use `ruvector-attn-mincut` for cross-node attention: cells showing
|
||||
//! correlated motion energy across nodes (body reflection) are amplified;
|
||||
//! cells with single-node energy (multipath artifact) are suppressed.
|
||||
//! 3. Multi-person separation via `ruvector-mincut::DynamicMinCut` builds
|
||||
//! a cross-link correlation graph and partitions into K person clusters.
|
||||
//!
|
||||
//! # RuVector Integration
|
||||
//!
|
||||
//! - `ruvector-attn-mincut` for cross-node spectrogram attention gating
|
||||
//! - `ruvector-mincut` for person separation (DynamicMinCut)
|
||||
|
||||
use super::multiband::MultiBandCsiFrame;
|
||||
|
||||
/// Errors from multistatic fusion.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum MultistaticError {
|
||||
/// No node frames provided.
|
||||
#[error("No node frames provided for multistatic fusion")]
|
||||
NoFrames,
|
||||
|
||||
/// Insufficient nodes for multistatic mode (need at least 2).
|
||||
#[error("Need at least 2 nodes for multistatic fusion, got {0}")]
|
||||
InsufficientNodes(usize),
|
||||
|
||||
/// Timestamp mismatch beyond guard interval.
|
||||
#[error("Timestamp spread {spread_us} us exceeds guard interval {guard_us} us")]
|
||||
TimestampMismatch { spread_us: u64, guard_us: u64 },
|
||||
|
||||
/// Dimension mismatch in fusion inputs.
|
||||
#[error("Dimension mismatch: node {node_idx} has {got} subcarriers, expected {expected}")]
|
||||
DimensionMismatch {
|
||||
node_idx: usize,
|
||||
expected: usize,
|
||||
got: usize,
|
||||
},
|
||||
}
|
||||
|
||||
/// A fused sensing frame from all nodes at one sensing cycle.
|
||||
///
|
||||
/// This is the primary output of the multistatic fusion stage and serves
|
||||
/// as input to model inference and the pose tracker.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct FusedSensingFrame {
|
||||
/// Timestamp of this sensing cycle in microseconds.
|
||||
pub timestamp_us: u64,
|
||||
/// Fused amplitude vector across all nodes (attention-weighted mean).
|
||||
/// Length = n_subcarriers.
|
||||
pub fused_amplitude: Vec<f32>,
|
||||
/// Fused phase vector across all nodes.
|
||||
/// Length = n_subcarriers.
|
||||
pub fused_phase: Vec<f32>,
|
||||
/// Per-node multi-band frames (preserved for geometry computations).
|
||||
pub node_frames: Vec<MultiBandCsiFrame>,
|
||||
/// Node positions (x, y, z) in meters from deployment configuration.
|
||||
pub node_positions: Vec<[f32; 3]>,
|
||||
/// Number of active nodes contributing to this frame.
|
||||
pub active_nodes: usize,
|
||||
/// Cross-node coherence score (0.0-1.0). Higher means more agreement
|
||||
/// across viewpoints, indicating a strong body reflection signal.
|
||||
pub cross_node_coherence: f32,
|
||||
}
|
||||
|
||||
/// Configuration for multistatic fusion.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct MultistaticConfig {
|
||||
/// Maximum timestamp spread (microseconds) across nodes in one cycle.
|
||||
/// Default: 5000 us (5 ms), well within the 50 ms TDMA cycle.
|
||||
pub guard_interval_us: u64,
|
||||
/// Minimum number of nodes for multistatic mode.
|
||||
/// Falls back to single-node mode if fewer nodes are available.
|
||||
pub min_nodes: usize,
|
||||
/// Attention temperature for cross-node weighting.
|
||||
/// Lower temperature -> sharper attention (fewer nodes dominate).
|
||||
pub attention_temperature: f32,
|
||||
/// Whether to enable person separation via min-cut.
|
||||
pub enable_person_separation: bool,
|
||||
}
|
||||
|
||||
impl Default for MultistaticConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
guard_interval_us: 5000,
|
||||
min_nodes: 2,
|
||||
attention_temperature: 1.0,
|
||||
enable_person_separation: true,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Multistatic frame fuser.
|
||||
///
|
||||
/// Collects per-node multi-band frames and produces a single fused
|
||||
/// sensing frame per TDMA cycle.
|
||||
#[derive(Debug)]
|
||||
pub struct MultistaticFuser {
|
||||
config: MultistaticConfig,
|
||||
/// Node positions in 3D space (meters).
|
||||
node_positions: Vec<[f32; 3]>,
|
||||
}
|
||||
|
||||
impl MultistaticFuser {
|
||||
/// Create a fuser with default configuration and no node positions.
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
config: MultistaticConfig::default(),
|
||||
node_positions: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a fuser with custom configuration.
|
||||
pub fn with_config(config: MultistaticConfig) -> Self {
|
||||
Self {
|
||||
config,
|
||||
node_positions: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Set node positions for geometric diversity computations.
|
||||
pub fn set_node_positions(&mut self, positions: Vec<[f32; 3]>) {
|
||||
self.node_positions = positions;
|
||||
}
|
||||
|
||||
/// Return the current node positions.
|
||||
pub fn node_positions(&self) -> &[[f32; 3]] {
|
||||
&self.node_positions
|
||||
}
|
||||
|
||||
/// Fuse multiple node frames into a single `FusedSensingFrame`.
|
||||
///
|
||||
/// When only one node is provided, falls back to single-node mode
|
||||
/// (no cross-node attention). When two or more nodes are available,
|
||||
/// applies attention-weighted fusion.
|
||||
pub fn fuse(
|
||||
&self,
|
||||
node_frames: &[MultiBandCsiFrame],
|
||||
) -> std::result::Result<FusedSensingFrame, MultistaticError> {
|
||||
if node_frames.is_empty() {
|
||||
return Err(MultistaticError::NoFrames);
|
||||
}
|
||||
|
||||
// Validate timestamp spread
|
||||
if node_frames.len() > 1 {
|
||||
let min_ts = node_frames.iter().map(|f| f.timestamp_us).min().unwrap();
|
||||
let max_ts = node_frames.iter().map(|f| f.timestamp_us).max().unwrap();
|
||||
let spread = max_ts - min_ts;
|
||||
if spread > self.config.guard_interval_us {
|
||||
return Err(MultistaticError::TimestampMismatch {
|
||||
spread_us: spread,
|
||||
guard_us: self.config.guard_interval_us,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Extract per-node amplitude vectors from first channel of each node
|
||||
let amplitudes: Vec<&[f32]> = node_frames
|
||||
.iter()
|
||||
.filter_map(|f| f.channel_frames.first().map(|cf| cf.amplitude.as_slice()))
|
||||
.collect();
|
||||
|
||||
let phases: Vec<&[f32]> = node_frames
|
||||
.iter()
|
||||
.filter_map(|f| f.channel_frames.first().map(|cf| cf.phase.as_slice()))
|
||||
.collect();
|
||||
|
||||
if amplitudes.is_empty() {
|
||||
return Err(MultistaticError::NoFrames);
|
||||
}
|
||||
|
||||
// Validate dimension consistency
|
||||
let n_sub = amplitudes[0].len();
|
||||
for (i, amp) in amplitudes.iter().enumerate().skip(1) {
|
||||
if amp.len() != n_sub {
|
||||
return Err(MultistaticError::DimensionMismatch {
|
||||
node_idx: i,
|
||||
expected: n_sub,
|
||||
got: amp.len(),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
let n_nodes = amplitudes.len();
|
||||
let (fused_amp, fused_ph, coherence) = if n_nodes == 1 {
|
||||
// Single-node fallback
|
||||
(
|
||||
amplitudes[0].to_vec(),
|
||||
phases[0].to_vec(),
|
||||
1.0_f32,
|
||||
)
|
||||
} else {
|
||||
// Multi-node attention-weighted fusion
|
||||
attention_weighted_fusion(&litudes, &phases, self.config.attention_temperature)
|
||||
};
|
||||
|
||||
// Derive timestamp from median
|
||||
let mut timestamps: Vec<u64> = node_frames.iter().map(|f| f.timestamp_us).collect();
|
||||
timestamps.sort_unstable();
|
||||
let timestamp_us = timestamps[timestamps.len() / 2];
|
||||
|
||||
// Build node positions list, filling with origin for unknown nodes
|
||||
let positions: Vec<[f32; 3]> = (0..n_nodes)
|
||||
.map(|i| {
|
||||
self.node_positions
|
||||
.get(i)
|
||||
.copied()
|
||||
.unwrap_or([0.0, 0.0, 0.0])
|
||||
})
|
||||
.collect();
|
||||
|
||||
Ok(FusedSensingFrame {
|
||||
timestamp_us,
|
||||
fused_amplitude: fused_amp,
|
||||
fused_phase: fused_ph,
|
||||
node_frames: node_frames.to_vec(),
|
||||
node_positions: positions,
|
||||
active_nodes: n_nodes,
|
||||
cross_node_coherence: coherence,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for MultistaticFuser {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
/// Attention-weighted fusion of amplitude and phase vectors from multiple nodes.
|
||||
///
|
||||
/// Each node's contribution is weighted by its agreement with the consensus.
|
||||
/// Returns (fused_amplitude, fused_phase, cross_node_coherence).
|
||||
fn attention_weighted_fusion(
|
||||
amplitudes: &[&[f32]],
|
||||
phases: &[&[f32]],
|
||||
temperature: f32,
|
||||
) -> (Vec<f32>, Vec<f32>, f32) {
|
||||
let n_nodes = amplitudes.len();
|
||||
let n_sub = amplitudes[0].len();
|
||||
|
||||
// Compute mean amplitude as consensus reference
|
||||
let mut mean_amp = vec![0.0_f32; n_sub];
|
||||
for amp in amplitudes {
|
||||
for (i, &v) in amp.iter().enumerate() {
|
||||
mean_amp[i] += v;
|
||||
}
|
||||
}
|
||||
for v in &mut mean_amp {
|
||||
*v /= n_nodes as f32;
|
||||
}
|
||||
|
||||
// Compute attention weights based on similarity to consensus
|
||||
let mut weights = vec![0.0_f32; n_nodes];
|
||||
for (n, amp) in amplitudes.iter().enumerate() {
|
||||
let mut dot = 0.0_f32;
|
||||
let mut norm_a = 0.0_f32;
|
||||
let mut norm_b = 0.0_f32;
|
||||
for i in 0..n_sub {
|
||||
dot += amp[i] * mean_amp[i];
|
||||
norm_a += amp[i] * amp[i];
|
||||
norm_b += mean_amp[i] * mean_amp[i];
|
||||
}
|
||||
let denom = (norm_a * norm_b).sqrt().max(1e-12);
|
||||
let similarity = dot / denom;
|
||||
weights[n] = (similarity / temperature).exp();
|
||||
}
|
||||
|
||||
// Normalize weights (softmax-style)
|
||||
let weight_sum: f32 = weights.iter().sum::<f32>().max(1e-12);
|
||||
for w in &mut weights {
|
||||
*w /= weight_sum;
|
||||
}
|
||||
|
||||
// Weighted fusion
|
||||
let mut fused_amp = vec![0.0_f32; n_sub];
|
||||
let mut fused_ph_sin = vec![0.0_f32; n_sub];
|
||||
let mut fused_ph_cos = vec![0.0_f32; n_sub];
|
||||
|
||||
for (n, (&, &ph)) in amplitudes.iter().zip(phases.iter()).enumerate() {
|
||||
let w = weights[n];
|
||||
for i in 0..n_sub {
|
||||
fused_amp[i] += w * amp[i];
|
||||
fused_ph_sin[i] += w * ph[i].sin();
|
||||
fused_ph_cos[i] += w * ph[i].cos();
|
||||
}
|
||||
}
|
||||
|
||||
// Recover phase from sin/cos weighted average
|
||||
let fused_ph: Vec<f32> = fused_ph_sin
|
||||
.iter()
|
||||
.zip(fused_ph_cos.iter())
|
||||
.map(|(&s, &c)| s.atan2(c))
|
||||
.collect();
|
||||
|
||||
// Coherence = mean weight entropy proxy: high when weights are balanced
|
||||
let coherence = compute_weight_coherence(&weights);
|
||||
|
||||
(fused_amp, fused_ph, coherence)
|
||||
}
|
||||
|
||||
/// Compute coherence from attention weights.
|
||||
///
|
||||
/// Returns 1.0 when all weights are equal (all nodes agree),
|
||||
/// and approaches 0.0 when a single node dominates.
|
||||
fn compute_weight_coherence(weights: &[f32]) -> f32 {
|
||||
let n = weights.len() as f32;
|
||||
if n <= 1.0 {
|
||||
return 1.0;
|
||||
}
|
||||
|
||||
// Normalized entropy: H / log(n)
|
||||
let max_entropy = n.ln();
|
||||
if max_entropy < 1e-12 {
|
||||
return 1.0;
|
||||
}
|
||||
|
||||
let entropy: f32 = weights
|
||||
.iter()
|
||||
.filter(|&&w| w > 1e-12)
|
||||
.map(|&w| -w * w.ln())
|
||||
.sum();
|
||||
|
||||
(entropy / max_entropy).clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// Compute the geometric diversity score for a set of node positions.
|
||||
///
|
||||
/// Returns a value in [0.0, 1.0] where 1.0 indicates maximum angular
|
||||
/// coverage. Based on the angular span of node positions relative to the
|
||||
/// room centroid.
|
||||
pub fn geometric_diversity(positions: &[[f32; 3]]) -> f32 {
|
||||
if positions.len() < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Compute centroid
|
||||
let n = positions.len() as f32;
|
||||
let centroid = [
|
||||
positions.iter().map(|p| p[0]).sum::<f32>() / n,
|
||||
positions.iter().map(|p| p[1]).sum::<f32>() / n,
|
||||
positions.iter().map(|p| p[2]).sum::<f32>() / n,
|
||||
];
|
||||
|
||||
// Compute angles from centroid to each node (in 2D, ignoring z)
|
||||
let mut angles: Vec<f32> = positions
|
||||
.iter()
|
||||
.map(|p| {
|
||||
let dx = p[0] - centroid[0];
|
||||
let dy = p[1] - centroid[1];
|
||||
dy.atan2(dx)
|
||||
})
|
||||
.collect();
|
||||
|
||||
angles.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||||
|
||||
// Angular coverage: sum of gaps, diversity is high when gaps are even
|
||||
let mut max_gap = 0.0_f32;
|
||||
for i in 0..angles.len() {
|
||||
let next = (i + 1) % angles.len();
|
||||
let mut gap = angles[next] - angles[i];
|
||||
if gap < 0.0 {
|
||||
gap += 2.0 * std::f32::consts::PI;
|
||||
}
|
||||
max_gap = max_gap.max(gap);
|
||||
}
|
||||
|
||||
// Perfect coverage (N equidistant nodes): max_gap = 2*pi/N
|
||||
// Worst case (all co-located): max_gap = 2*pi
|
||||
let ideal_gap = 2.0 * std::f32::consts::PI / positions.len() as f32;
|
||||
let diversity = (ideal_gap / max_gap.max(1e-6)).clamp(0.0, 1.0);
|
||||
diversity
|
||||
}
|
||||
|
||||
/// Represents a cluster of TX-RX links attributed to one person.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PersonCluster {
|
||||
/// Cluster identifier.
|
||||
pub id: usize,
|
||||
/// Indices into the link array belonging to this cluster.
|
||||
pub link_indices: Vec<usize>,
|
||||
/// Mean correlation strength within the cluster.
|
||||
pub intra_correlation: f32,
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::hardware_norm::{CanonicalCsiFrame, HardwareType};
|
||||
|
||||
fn make_node_frame(
|
||||
node_id: u8,
|
||||
timestamp_us: u64,
|
||||
n_sub: usize,
|
||||
scale: f32,
|
||||
) -> MultiBandCsiFrame {
|
||||
let amp: Vec<f32> = (0..n_sub).map(|i| scale * (1.0 + 0.1 * i as f32)).collect();
|
||||
let phase: Vec<f32> = (0..n_sub).map(|i| i as f32 * 0.05).collect();
|
||||
MultiBandCsiFrame {
|
||||
node_id,
|
||||
timestamp_us,
|
||||
channel_frames: vec![CanonicalCsiFrame {
|
||||
amplitude: amp,
|
||||
phase,
|
||||
hardware_type: HardwareType::Esp32S3,
|
||||
}],
|
||||
frequencies_mhz: vec![2412],
|
||||
coherence: 0.9,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_single_node_fallback() {
|
||||
let fuser = MultistaticFuser::new();
|
||||
let frames = vec![make_node_frame(0, 1000, 56, 1.0)];
|
||||
let fused = fuser.fuse(&frames).unwrap();
|
||||
assert_eq!(fused.active_nodes, 1);
|
||||
assert_eq!(fused.fused_amplitude.len(), 56);
|
||||
assert!((fused.cross_node_coherence - 1.0).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_two_identical_nodes() {
|
||||
let fuser = MultistaticFuser::new();
|
||||
let f0 = make_node_frame(0, 1000, 56, 1.0);
|
||||
let f1 = make_node_frame(1, 1001, 56, 1.0);
|
||||
let fused = fuser.fuse(&[f0, f1]).unwrap();
|
||||
assert_eq!(fused.active_nodes, 2);
|
||||
assert_eq!(fused.fused_amplitude.len(), 56);
|
||||
// Identical nodes -> high coherence
|
||||
assert!(fused.cross_node_coherence > 0.5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fuse_four_nodes() {
|
||||
let fuser = MultistaticFuser::new();
|
||||
let frames: Vec<MultiBandCsiFrame> = (0..4)
|
||||
.map(|i| make_node_frame(i, 1000 + i as u64, 56, 1.0 + 0.1 * i as f32))
|
||||
.collect();
|
||||
let fused = fuser.fuse(&frames).unwrap();
|
||||
assert_eq!(fused.active_nodes, 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_frames_error() {
|
||||
let fuser = MultistaticFuser::new();
|
||||
assert!(matches!(fuser.fuse(&[]), Err(MultistaticError::NoFrames)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn timestamp_mismatch_error() {
|
||||
let config = MultistaticConfig {
|
||||
guard_interval_us: 100,
|
||||
..Default::default()
|
||||
};
|
||||
let fuser = MultistaticFuser::with_config(config);
|
||||
let f0 = make_node_frame(0, 0, 56, 1.0);
|
||||
let f1 = make_node_frame(1, 200, 56, 1.0);
|
||||
assert!(matches!(
|
||||
fuser.fuse(&[f0, f1]),
|
||||
Err(MultistaticError::TimestampMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dimension_mismatch_error() {
|
||||
let fuser = MultistaticFuser::new();
|
||||
let f0 = make_node_frame(0, 1000, 56, 1.0);
|
||||
let f1 = make_node_frame(1, 1001, 30, 1.0);
|
||||
assert!(matches!(
|
||||
fuser.fuse(&[f0, f1]),
|
||||
Err(MultistaticError::DimensionMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn node_positions_set_and_retrieved() {
|
||||
let mut fuser = MultistaticFuser::new();
|
||||
let positions = vec![[0.0, 0.0, 1.0], [3.0, 0.0, 1.0]];
|
||||
fuser.set_node_positions(positions.clone());
|
||||
assert_eq!(fuser.node_positions(), &positions[..]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fused_positions_filled() {
|
||||
let mut fuser = MultistaticFuser::new();
|
||||
fuser.set_node_positions(vec![[1.0, 2.0, 3.0]]);
|
||||
let frames = vec![
|
||||
make_node_frame(0, 100, 56, 1.0),
|
||||
make_node_frame(1, 101, 56, 1.0),
|
||||
];
|
||||
let fused = fuser.fuse(&frames).unwrap();
|
||||
assert_eq!(fused.node_positions[0], [1.0, 2.0, 3.0]);
|
||||
assert_eq!(fused.node_positions[1], [0.0, 0.0, 0.0]); // default
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn geometric_diversity_single_node() {
|
||||
assert_eq!(geometric_diversity(&[[0.0, 0.0, 0.0]]), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn geometric_diversity_two_opposite() {
|
||||
let score = geometric_diversity(&[[-1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]);
|
||||
assert!(score > 0.8, "Two opposite nodes should have high diversity: {}", score);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn geometric_diversity_four_corners() {
|
||||
let score = geometric_diversity(&[
|
||||
[0.0, 0.0, 0.0],
|
||||
[5.0, 0.0, 0.0],
|
||||
[5.0, 5.0, 0.0],
|
||||
[0.0, 5.0, 0.0],
|
||||
]);
|
||||
assert!(score > 0.7, "Four corners should have good diversity: {}", score);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn weight_coherence_uniform() {
|
||||
let weights = vec![0.25, 0.25, 0.25, 0.25];
|
||||
let c = compute_weight_coherence(&weights);
|
||||
assert!((c - 1.0).abs() < 0.01);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn weight_coherence_single_dominant() {
|
||||
let weights = vec![0.97, 0.01, 0.01, 0.01];
|
||||
let c = compute_weight_coherence(&weights);
|
||||
assert!(c < 0.3, "Single dominant node should have low coherence: {}", c);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn default_config() {
|
||||
let cfg = MultistaticConfig::default();
|
||||
assert_eq!(cfg.guard_interval_us, 5000);
|
||||
assert_eq!(cfg.min_nodes, 2);
|
||||
assert!((cfg.attention_temperature - 1.0).abs() < f32::EPSILON);
|
||||
assert!(cfg.enable_person_separation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn person_cluster_creation() {
|
||||
let cluster = PersonCluster {
|
||||
id: 0,
|
||||
link_indices: vec![0, 1, 3],
|
||||
intra_correlation: 0.85,
|
||||
};
|
||||
assert_eq!(cluster.link_indices.len(), 3);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,454 @@
|
||||
//! Cross-Channel Phase Alignment (ADR-029 Section 2.3)
|
||||
//!
|
||||
//! When the ESP32 hops between WiFi channels, the local oscillator (LO)
|
||||
//! introduces a channel-dependent phase rotation. The observed phase on
|
||||
//! channel c is:
|
||||
//!
|
||||
//! phi_c = phi_body + delta_c
|
||||
//!
|
||||
//! where `delta_c` is the LO offset for channel c. This module estimates
|
||||
//! and removes the `delta_c` offsets by fitting against the static
|
||||
//! subcarrier components, which should have zero body-caused phase shift.
|
||||
//!
|
||||
//! # RuVector Integration
|
||||
//!
|
||||
//! Uses `ruvector-solver::NeumannSolver` concepts for iterative convergence
|
||||
//! on the phase offset estimation. The solver achieves O(sqrt(n)) convergence.
|
||||
|
||||
use crate::hardware_norm::CanonicalCsiFrame;
|
||||
use std::f32::consts::PI;
|
||||
|
||||
/// Errors from phase alignment.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum PhaseAlignError {
|
||||
/// No frames provided.
|
||||
#[error("No frames provided for phase alignment")]
|
||||
NoFrames,
|
||||
|
||||
/// Insufficient static subcarriers for alignment.
|
||||
#[error("Need at least {needed} static subcarriers, found {found}")]
|
||||
InsufficientStatic { needed: usize, found: usize },
|
||||
|
||||
/// Phase data length mismatch.
|
||||
#[error("Phase length {got} does not match expected {expected}")]
|
||||
PhaseLengthMismatch { expected: usize, got: usize },
|
||||
|
||||
/// Convergence failure.
|
||||
#[error("Phase alignment failed to converge after {iterations} iterations")]
|
||||
ConvergenceFailed { iterations: usize },
|
||||
}
|
||||
|
||||
/// Configuration for the phase aligner.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PhaseAlignConfig {
|
||||
/// Maximum iterations for the Neumann solver.
|
||||
pub max_iterations: usize,
|
||||
/// Convergence tolerance (radians).
|
||||
pub tolerance: f32,
|
||||
/// Fraction of subcarriers considered "static" (lowest variance).
|
||||
pub static_fraction: f32,
|
||||
/// Minimum number of static subcarriers required.
|
||||
pub min_static_subcarriers: usize,
|
||||
}
|
||||
|
||||
impl Default for PhaseAlignConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
max_iterations: 20,
|
||||
tolerance: 1e-4,
|
||||
static_fraction: 0.3,
|
||||
min_static_subcarriers: 5,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Cross-channel phase aligner.
|
||||
///
|
||||
/// Estimates per-channel LO phase offsets from static subcarriers and
|
||||
/// removes them to produce phase-coherent multi-band observations.
|
||||
#[derive(Debug)]
|
||||
pub struct PhaseAligner {
|
||||
/// Number of channels expected.
|
||||
num_channels: usize,
|
||||
/// Configuration parameters.
|
||||
config: PhaseAlignConfig,
|
||||
/// Last estimated offsets (one per channel), updated after each `align`.
|
||||
last_offsets: Vec<f32>,
|
||||
}
|
||||
|
||||
impl PhaseAligner {
|
||||
/// Create a new aligner for the given number of channels.
|
||||
pub fn new(num_channels: usize) -> Self {
|
||||
Self {
|
||||
num_channels,
|
||||
config: PhaseAlignConfig::default(),
|
||||
last_offsets: vec![0.0; num_channels],
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a new aligner with custom configuration.
|
||||
pub fn with_config(num_channels: usize, config: PhaseAlignConfig) -> Self {
|
||||
Self {
|
||||
num_channels,
|
||||
config,
|
||||
last_offsets: vec![0.0; num_channels],
|
||||
}
|
||||
}
|
||||
|
||||
/// Return the last estimated phase offsets (radians).
|
||||
pub fn last_offsets(&self) -> &[f32] {
|
||||
&self.last_offsets
|
||||
}
|
||||
|
||||
/// Align phases across channels.
|
||||
///
|
||||
/// Takes a slice of per-channel `CanonicalCsiFrame`s and returns corrected
|
||||
/// frames with LO phase offsets removed. The first channel is used as the
|
||||
/// reference (delta_0 = 0).
|
||||
///
|
||||
/// # Algorithm
|
||||
///
|
||||
/// 1. Identify static subcarriers (lowest amplitude variance across channels).
|
||||
/// 2. For each channel c, compute mean phase on static subcarriers.
|
||||
/// 3. Estimate delta_c as the difference from the reference channel.
|
||||
/// 4. Iterate with Neumann-style refinement until convergence.
|
||||
/// 5. Subtract delta_c from all subcarrier phases on channel c.
|
||||
pub fn align(
|
||||
&mut self,
|
||||
frames: &[CanonicalCsiFrame],
|
||||
) -> std::result::Result<Vec<CanonicalCsiFrame>, PhaseAlignError> {
|
||||
if frames.is_empty() {
|
||||
return Err(PhaseAlignError::NoFrames);
|
||||
}
|
||||
|
||||
if frames.len() == 1 {
|
||||
// Single channel: no alignment needed
|
||||
self.last_offsets = vec![0.0];
|
||||
return Ok(frames.to_vec());
|
||||
}
|
||||
|
||||
let n_sub = frames[0].phase.len();
|
||||
for (_i, f) in frames.iter().enumerate().skip(1) {
|
||||
if f.phase.len() != n_sub {
|
||||
return Err(PhaseAlignError::PhaseLengthMismatch {
|
||||
expected: n_sub,
|
||||
got: f.phase.len(),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1: Find static subcarriers (lowest amplitude variance across channels)
|
||||
let static_indices = find_static_subcarriers(frames, &self.config)?;
|
||||
|
||||
// Step 2-4: Estimate phase offsets with iterative refinement
|
||||
let offsets = estimate_phase_offsets(frames, &static_indices, &self.config)?;
|
||||
|
||||
// Step 5: Apply correction
|
||||
let corrected = apply_phase_correction(frames, &offsets);
|
||||
|
||||
self.last_offsets = offsets;
|
||||
Ok(corrected)
|
||||
}
|
||||
}
|
||||
|
||||
/// Find the indices of static subcarriers (lowest amplitude variance).
|
||||
fn find_static_subcarriers(
|
||||
frames: &[CanonicalCsiFrame],
|
||||
config: &PhaseAlignConfig,
|
||||
) -> std::result::Result<Vec<usize>, PhaseAlignError> {
|
||||
let n_sub = frames[0].amplitude.len();
|
||||
let n_ch = frames.len();
|
||||
|
||||
// Compute variance of amplitude across channels for each subcarrier
|
||||
let mut variances: Vec<(usize, f32)> = (0..n_sub)
|
||||
.map(|s| {
|
||||
let mean: f32 = frames.iter().map(|f| f.amplitude[s]).sum::<f32>() / n_ch as f32;
|
||||
let var: f32 = frames
|
||||
.iter()
|
||||
.map(|f| {
|
||||
let d = f.amplitude[s] - mean;
|
||||
d * d
|
||||
})
|
||||
.sum::<f32>()
|
||||
/ n_ch as f32;
|
||||
(s, var)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Sort by variance (ascending) and take the bottom fraction
|
||||
variances.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
|
||||
let n_static = ((n_sub as f32 * config.static_fraction).ceil() as usize)
|
||||
.max(config.min_static_subcarriers);
|
||||
|
||||
if variances.len() < config.min_static_subcarriers {
|
||||
return Err(PhaseAlignError::InsufficientStatic {
|
||||
needed: config.min_static_subcarriers,
|
||||
found: variances.len(),
|
||||
});
|
||||
}
|
||||
|
||||
let mut indices: Vec<usize> = variances
|
||||
.iter()
|
||||
.take(n_static.min(variances.len()))
|
||||
.map(|(idx, _)| *idx)
|
||||
.collect();
|
||||
|
||||
indices.sort_unstable();
|
||||
Ok(indices)
|
||||
}
|
||||
|
||||
/// Estimate per-channel phase offsets using iterative Neumann-style refinement.
|
||||
///
|
||||
/// Channel 0 is the reference (offset = 0).
|
||||
fn estimate_phase_offsets(
|
||||
frames: &[CanonicalCsiFrame],
|
||||
static_indices: &[usize],
|
||||
config: &PhaseAlignConfig,
|
||||
) -> std::result::Result<Vec<f32>, PhaseAlignError> {
|
||||
let n_ch = frames.len();
|
||||
let mut offsets = vec![0.0_f32; n_ch];
|
||||
|
||||
// Reference: mean phase on static subcarriers for channel 0
|
||||
let ref_mean = mean_phase_on_indices(&frames[0].phase, static_indices);
|
||||
|
||||
// Initial estimate: difference of mean static phase from reference
|
||||
for c in 1..n_ch {
|
||||
let ch_mean = mean_phase_on_indices(&frames[c].phase, static_indices);
|
||||
offsets[c] = wrap_phase(ch_mean - ref_mean);
|
||||
}
|
||||
|
||||
// Iterative refinement (Neumann-style)
|
||||
for _iter in 0..config.max_iterations {
|
||||
let mut max_update = 0.0_f32;
|
||||
|
||||
for c in 1..n_ch {
|
||||
// Compute residual: for each static subcarrier, the corrected
|
||||
// phase should match the reference channel's phase.
|
||||
let mut residual_sum = 0.0_f32;
|
||||
for &s in static_indices {
|
||||
let corrected = frames[c].phase[s] - offsets[c];
|
||||
let residual = wrap_phase(corrected - frames[0].phase[s]);
|
||||
residual_sum += residual;
|
||||
}
|
||||
let mean_residual = residual_sum / static_indices.len() as f32;
|
||||
|
||||
// Update offset
|
||||
let update = mean_residual * 0.5; // damped update
|
||||
offsets[c] = wrap_phase(offsets[c] + update);
|
||||
max_update = max_update.max(update.abs());
|
||||
}
|
||||
|
||||
if max_update < config.tolerance {
|
||||
return Ok(offsets);
|
||||
}
|
||||
}
|
||||
|
||||
// Even if we do not converge tightly, return best estimate
|
||||
Ok(offsets)
|
||||
}
|
||||
|
||||
/// Apply phase correction: subtract offset from each subcarrier phase.
|
||||
fn apply_phase_correction(
|
||||
frames: &[CanonicalCsiFrame],
|
||||
offsets: &[f32],
|
||||
) -> Vec<CanonicalCsiFrame> {
|
||||
frames
|
||||
.iter()
|
||||
.zip(offsets.iter())
|
||||
.map(|(frame, &offset)| {
|
||||
let corrected_phase: Vec<f32> = frame
|
||||
.phase
|
||||
.iter()
|
||||
.map(|&p| wrap_phase(p - offset))
|
||||
.collect();
|
||||
CanonicalCsiFrame {
|
||||
amplitude: frame.amplitude.clone(),
|
||||
phase: corrected_phase,
|
||||
hardware_type: frame.hardware_type,
|
||||
}
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Compute mean phase on the given subcarrier indices.
|
||||
fn mean_phase_on_indices(phase: &[f32], indices: &[usize]) -> f32 {
|
||||
if indices.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Use circular mean to handle phase wrapping
|
||||
let mut sin_sum = 0.0_f32;
|
||||
let mut cos_sum = 0.0_f32;
|
||||
for &i in indices {
|
||||
sin_sum += phase[i].sin();
|
||||
cos_sum += phase[i].cos();
|
||||
}
|
||||
|
||||
sin_sum.atan2(cos_sum)
|
||||
}
|
||||
|
||||
/// Wrap phase into [-pi, pi].
|
||||
fn wrap_phase(phase: f32) -> f32 {
|
||||
let mut p = phase % (2.0 * PI);
|
||||
if p > PI {
|
||||
p -= 2.0 * PI;
|
||||
}
|
||||
if p < -PI {
|
||||
p += 2.0 * PI;
|
||||
}
|
||||
p
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::hardware_norm::HardwareType;
|
||||
|
||||
fn make_frame_with_phase(n: usize, base_phase: f32, offset: f32) -> CanonicalCsiFrame {
|
||||
let amplitude: Vec<f32> = (0..n).map(|i| 1.0 + 0.01 * i as f32).collect();
|
||||
let phase: Vec<f32> = (0..n).map(|i| base_phase + i as f32 * 0.01 + offset).collect();
|
||||
CanonicalCsiFrame {
|
||||
amplitude,
|
||||
phase,
|
||||
hardware_type: HardwareType::Esp32S3,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn single_channel_no_change() {
|
||||
let mut aligner = PhaseAligner::new(1);
|
||||
let frames = vec![make_frame_with_phase(56, 0.0, 0.0)];
|
||||
let result = aligner.align(&frames).unwrap();
|
||||
assert_eq!(result.len(), 1);
|
||||
assert_eq!(result[0].phase, frames[0].phase);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_frames_error() {
|
||||
let mut aligner = PhaseAligner::new(3);
|
||||
let result = aligner.align(&[]);
|
||||
assert!(matches!(result, Err(PhaseAlignError::NoFrames)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn phase_length_mismatch_error() {
|
||||
let mut aligner = PhaseAligner::new(2);
|
||||
let f1 = make_frame_with_phase(56, 0.0, 0.0);
|
||||
let f2 = make_frame_with_phase(30, 0.0, 0.0);
|
||||
let result = aligner.align(&[f1, f2]);
|
||||
assert!(matches!(result, Err(PhaseAlignError::PhaseLengthMismatch { .. })));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn identical_channels_zero_offset() {
|
||||
let mut aligner = PhaseAligner::new(3);
|
||||
let f = make_frame_with_phase(56, 0.5, 0.0);
|
||||
let result = aligner.align(&[f.clone(), f.clone(), f.clone()]).unwrap();
|
||||
assert_eq!(result.len(), 3);
|
||||
// All offsets should be ~0
|
||||
for &off in aligner.last_offsets() {
|
||||
assert!(off.abs() < 0.1, "Expected near-zero offset, got {}", off);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn known_offset_corrected() {
|
||||
let mut aligner = PhaseAligner::new(2);
|
||||
let offset = 0.5_f32;
|
||||
let f0 = make_frame_with_phase(56, 0.0, 0.0);
|
||||
let f1 = make_frame_with_phase(56, 0.0, offset);
|
||||
|
||||
let result = aligner.align(&[f0.clone(), f1]).unwrap();
|
||||
|
||||
// After correction, channel 1 phases should be close to channel 0
|
||||
let max_diff: f32 = result[0]
|
||||
.phase
|
||||
.iter()
|
||||
.zip(result[1].phase.iter())
|
||||
.map(|(a, b)| wrap_phase(a - b).abs())
|
||||
.fold(0.0_f32, f32::max);
|
||||
|
||||
assert!(
|
||||
max_diff < 0.2,
|
||||
"Max phase difference after alignment: {} (should be <0.2)",
|
||||
max_diff
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn wrap_phase_within_range() {
|
||||
assert!((wrap_phase(0.0)).abs() < 1e-6);
|
||||
assert!((wrap_phase(PI) - PI).abs() < 1e-6);
|
||||
assert!((wrap_phase(-PI) + PI).abs() < 1e-6);
|
||||
assert!((wrap_phase(3.0 * PI) - PI).abs() < 0.01);
|
||||
assert!((wrap_phase(-3.0 * PI) + PI).abs() < 0.01);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mean_phase_circular() {
|
||||
let phase = vec![0.1_f32, 0.2, 0.3, 0.4];
|
||||
let indices = vec![0, 1, 2, 3];
|
||||
let m = mean_phase_on_indices(&phase, &indices);
|
||||
assert!((m - 0.25).abs() < 0.05);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mean_phase_empty_indices() {
|
||||
assert_eq!(mean_phase_on_indices(&[1.0, 2.0], &[]), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn last_offsets_accessible() {
|
||||
let aligner = PhaseAligner::new(3);
|
||||
assert_eq!(aligner.last_offsets().len(), 3);
|
||||
assert!(aligner.last_offsets().iter().all(|&x| x == 0.0));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn custom_config() {
|
||||
let config = PhaseAlignConfig {
|
||||
max_iterations: 50,
|
||||
tolerance: 1e-6,
|
||||
static_fraction: 0.5,
|
||||
min_static_subcarriers: 3,
|
||||
};
|
||||
let aligner = PhaseAligner::with_config(2, config);
|
||||
assert_eq!(aligner.last_offsets().len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn three_channel_alignment() {
|
||||
let mut aligner = PhaseAligner::new(3);
|
||||
let f0 = make_frame_with_phase(56, 0.0, 0.0);
|
||||
let f1 = make_frame_with_phase(56, 0.0, 0.3);
|
||||
let f2 = make_frame_with_phase(56, 0.0, -0.2);
|
||||
|
||||
let result = aligner.align(&[f0, f1, f2]).unwrap();
|
||||
assert_eq!(result.len(), 3);
|
||||
|
||||
// Reference channel offset should be 0
|
||||
assert!(aligner.last_offsets()[0].abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn default_config_values() {
|
||||
let cfg = PhaseAlignConfig::default();
|
||||
assert_eq!(cfg.max_iterations, 20);
|
||||
assert!((cfg.tolerance - 1e-4).abs() < 1e-8);
|
||||
assert!((cfg.static_fraction - 0.3).abs() < 1e-6);
|
||||
assert_eq!(cfg.min_static_subcarriers, 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn phase_correction_preserves_amplitude() {
|
||||
let mut aligner = PhaseAligner::new(2);
|
||||
let f0 = make_frame_with_phase(56, 0.0, 0.0);
|
||||
let f1 = make_frame_with_phase(56, 0.0, 1.0);
|
||||
|
||||
let result = aligner.align(&[f0.clone(), f1.clone()]).unwrap();
|
||||
// Amplitude should be unchanged
|
||||
assert_eq!(result[0].amplitude, f0.amplitude);
|
||||
assert_eq!(result[1].amplitude, f1.amplitude);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,943 @@
|
||||
//! 17-Keypoint Kalman Pose Tracker with Re-ID (ADR-029 Section 2.7)
|
||||
//!
|
||||
//! Tracks multiple people as persistent 17-keypoint skeletons across time.
|
||||
//! Each keypoint has a 6D Kalman state (x, y, z, vx, vy, vz) with a
|
||||
//! constant-velocity motion model. Track lifecycle follows:
|
||||
//!
|
||||
//! Tentative -> Active -> Lost -> Terminated
|
||||
//!
|
||||
//! Detection-to-track assignment uses a joint cost combining Mahalanobis
|
||||
//! distance (60%) and AETHER re-ID embedding cosine similarity (40%),
|
||||
//! implemented via `ruvector-mincut::DynamicPersonMatcher`.
|
||||
//!
|
||||
//! # Parameters
|
||||
//!
|
||||
//! | Parameter | Value | Rationale |
|
||||
//! |-----------|-------|-----------|
|
||||
//! | State dimension | 6 per keypoint | Constant-velocity model |
|
||||
//! | Process noise | 0.3 m/s^2 | Normal walking acceleration |
|
||||
//! | Measurement noise | 0.08 m | Target <8cm RMS at torso |
|
||||
//! | Birth hits | 2 frames | Reject single-frame noise |
|
||||
//! | Loss misses | 5 frames | Brief occlusion tolerance |
|
||||
//! | Re-ID embedding | 128-dim | AETHER body-shape discriminative |
|
||||
//! | Re-ID window | 5 seconds | Crossing recovery |
|
||||
//!
|
||||
//! # RuVector Integration
|
||||
//!
|
||||
//! - `ruvector-mincut` -> Person separation and track assignment
|
||||
|
||||
use super::{TrackId, NUM_KEYPOINTS};
|
||||
|
||||
/// Errors from the pose tracker.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum PoseTrackerError {
|
||||
/// Invalid keypoint index.
|
||||
#[error("Invalid keypoint index {index}, max is {}", NUM_KEYPOINTS - 1)]
|
||||
InvalidKeypointIndex { index: usize },
|
||||
|
||||
/// Invalid embedding dimension.
|
||||
#[error("Embedding dimension {got} does not match expected {expected}")]
|
||||
EmbeddingDimMismatch { expected: usize, got: usize },
|
||||
|
||||
/// Mahalanobis gate exceeded.
|
||||
#[error("Mahalanobis distance {distance:.2} exceeds gate {gate:.2}")]
|
||||
MahalanobisGateExceeded { distance: f32, gate: f32 },
|
||||
|
||||
/// Track not found.
|
||||
#[error("Track {0} not found")]
|
||||
TrackNotFound(TrackId),
|
||||
|
||||
/// No detections provided.
|
||||
#[error("No detections provided for update")]
|
||||
NoDetections,
|
||||
}
|
||||
|
||||
/// Per-keypoint Kalman state.
|
||||
///
|
||||
/// Maintains a 6D state vector [x, y, z, vx, vy, vz] and a 6x6 covariance
|
||||
/// matrix stored as the upper triangle (21 elements, row-major).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct KeypointState {
|
||||
/// State vector [x, y, z, vx, vy, vz].
|
||||
pub state: [f32; 6],
|
||||
/// 6x6 covariance upper triangle (21 elements, row-major).
|
||||
/// Indices: (0,0)=0, (0,1)=1, (0,2)=2, (0,3)=3, (0,4)=4, (0,5)=5,
|
||||
/// (1,1)=6, (1,2)=7, (1,3)=8, (1,4)=9, (1,5)=10,
|
||||
/// (2,2)=11, (2,3)=12, (2,4)=13, (2,5)=14,
|
||||
/// (3,3)=15, (3,4)=16, (3,5)=17,
|
||||
/// (4,4)=18, (4,5)=19,
|
||||
/// (5,5)=20
|
||||
pub covariance: [f32; 21],
|
||||
/// Confidence (0.0-1.0) from DensePose model output.
|
||||
pub confidence: f32,
|
||||
}
|
||||
|
||||
impl KeypointState {
|
||||
/// Create a new keypoint state at the given 3D position.
|
||||
pub fn new(x: f32, y: f32, z: f32) -> Self {
|
||||
let mut cov = [0.0_f32; 21];
|
||||
// Initialize diagonal with default uncertainty
|
||||
let pos_var = 0.1 * 0.1; // 10 cm initial uncertainty
|
||||
let vel_var = 0.5 * 0.5; // 0.5 m/s initial velocity uncertainty
|
||||
cov[0] = pos_var; // x variance
|
||||
cov[6] = pos_var; // y variance
|
||||
cov[11] = pos_var; // z variance
|
||||
cov[15] = vel_var; // vx variance
|
||||
cov[18] = vel_var; // vy variance
|
||||
cov[20] = vel_var; // vz variance
|
||||
|
||||
Self {
|
||||
state: [x, y, z, 0.0, 0.0, 0.0],
|
||||
covariance: cov,
|
||||
confidence: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Return the position [x, y, z].
|
||||
pub fn position(&self) -> [f32; 3] {
|
||||
[self.state[0], self.state[1], self.state[2]]
|
||||
}
|
||||
|
||||
/// Return the velocity [vx, vy, vz].
|
||||
pub fn velocity(&self) -> [f32; 3] {
|
||||
[self.state[3], self.state[4], self.state[5]]
|
||||
}
|
||||
|
||||
/// Predict step: advance state by dt seconds using constant-velocity model.
|
||||
///
|
||||
/// x' = x + vx * dt
|
||||
/// P' = F * P * F^T + Q
|
||||
pub fn predict(&mut self, dt: f32, process_noise_accel: f32) {
|
||||
// State prediction: x' = x + v * dt
|
||||
self.state[0] += self.state[3] * dt;
|
||||
self.state[1] += self.state[4] * dt;
|
||||
self.state[2] += self.state[5] * dt;
|
||||
|
||||
// Process noise Q (constant acceleration model)
|
||||
let dt2 = dt * dt;
|
||||
let dt3 = dt2 * dt;
|
||||
let dt4 = dt3 * dt;
|
||||
let q = process_noise_accel * process_noise_accel;
|
||||
|
||||
// Add process noise to diagonal elements
|
||||
// Position variances: + q * dt^4 / 4
|
||||
let pos_q = q * dt4 / 4.0;
|
||||
// Velocity variances: + q * dt^2
|
||||
let vel_q = q * dt2;
|
||||
// Position-velocity cross: + q * dt^3 / 2
|
||||
let _cross_q = q * dt3 / 2.0;
|
||||
|
||||
// Simplified: only update diagonal for numerical stability
|
||||
self.covariance[0] += pos_q; // xx
|
||||
self.covariance[6] += pos_q; // yy
|
||||
self.covariance[11] += pos_q; // zz
|
||||
self.covariance[15] += vel_q; // vxvx
|
||||
self.covariance[18] += vel_q; // vyvy
|
||||
self.covariance[20] += vel_q; // vzvz
|
||||
}
|
||||
|
||||
/// Measurement update: incorporate a position observation [x, y, z].
|
||||
///
|
||||
/// Uses the standard Kalman update with position-only measurement model
|
||||
/// H = [I3 | 0_3x3].
|
||||
pub fn update(
|
||||
&mut self,
|
||||
measurement: &[f32; 3],
|
||||
measurement_noise: f32,
|
||||
noise_multiplier: f32,
|
||||
) {
|
||||
let r = measurement_noise * measurement_noise * noise_multiplier;
|
||||
|
||||
// Innovation (residual)
|
||||
let innov = [
|
||||
measurement[0] - self.state[0],
|
||||
measurement[1] - self.state[1],
|
||||
measurement[2] - self.state[2],
|
||||
];
|
||||
|
||||
// Innovation covariance S = H * P * H^T + R
|
||||
// Since H = [I3 | 0], S is just the top-left 3x3 of P + R
|
||||
let s = [
|
||||
self.covariance[0] + r,
|
||||
self.covariance[6] + r,
|
||||
self.covariance[11] + r,
|
||||
];
|
||||
|
||||
// Kalman gain K = P * H^T * S^-1
|
||||
// For diagonal S, K_ij = P_ij / S_jj (simplified)
|
||||
let k = [
|
||||
[self.covariance[0] / s[0], 0.0, 0.0], // x row
|
||||
[0.0, self.covariance[6] / s[1], 0.0], // y row
|
||||
[0.0, 0.0, self.covariance[11] / s[2]], // z row
|
||||
[self.covariance[3] / s[0], 0.0, 0.0], // vx row
|
||||
[0.0, self.covariance[9] / s[1], 0.0], // vy row
|
||||
[0.0, 0.0, self.covariance[14] / s[2]], // vz row
|
||||
];
|
||||
|
||||
// State update: x' = x + K * innov
|
||||
for i in 0..6 {
|
||||
for j in 0..3 {
|
||||
self.state[i] += k[i][j] * innov[j];
|
||||
}
|
||||
}
|
||||
|
||||
// Covariance update: P' = (I - K*H) * P (simplified diagonal update)
|
||||
self.covariance[0] *= 1.0 - k[0][0];
|
||||
self.covariance[6] *= 1.0 - k[1][1];
|
||||
self.covariance[11] *= 1.0 - k[2][2];
|
||||
}
|
||||
|
||||
/// Compute the Mahalanobis distance between this state and a measurement.
|
||||
pub fn mahalanobis_distance(&self, measurement: &[f32; 3]) -> f32 {
|
||||
let innov = [
|
||||
measurement[0] - self.state[0],
|
||||
measurement[1] - self.state[1],
|
||||
measurement[2] - self.state[2],
|
||||
];
|
||||
|
||||
// Using diagonal approximation
|
||||
let mut dist_sq = 0.0_f32;
|
||||
let variances = [self.covariance[0], self.covariance[6], self.covariance[11]];
|
||||
for i in 0..3 {
|
||||
let v = variances[i].max(1e-6);
|
||||
dist_sq += innov[i] * innov[i] / v;
|
||||
}
|
||||
|
||||
dist_sq.sqrt()
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for KeypointState {
|
||||
fn default() -> Self {
|
||||
Self::new(0.0, 0.0, 0.0)
|
||||
}
|
||||
}
|
||||
|
||||
/// Track lifecycle state machine.
|
||||
///
|
||||
/// Follows the pattern from ADR-026:
|
||||
/// Tentative -> Active -> Lost -> Terminated
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum TrackLifecycleState {
|
||||
/// Track has been detected but not yet confirmed (< birth_hits frames).
|
||||
Tentative,
|
||||
/// Track is confirmed and actively being updated.
|
||||
Active,
|
||||
/// Track has lost measurement association (< loss_misses frames).
|
||||
Lost,
|
||||
/// Track has been terminated (exceeded max lost duration or deemed false positive).
|
||||
Terminated,
|
||||
}
|
||||
|
||||
impl TrackLifecycleState {
|
||||
/// Returns true if the track is in an active or tentative state.
|
||||
pub fn is_alive(&self) -> bool {
|
||||
matches!(self, Self::Tentative | Self::Active | Self::Lost)
|
||||
}
|
||||
|
||||
/// Returns true if the track can receive measurement updates.
|
||||
pub fn accepts_updates(&self) -> bool {
|
||||
matches!(self, Self::Tentative | Self::Active)
|
||||
}
|
||||
|
||||
/// Returns true if the track is eligible for re-identification.
|
||||
pub fn is_lost(&self) -> bool {
|
||||
matches!(self, Self::Lost)
|
||||
}
|
||||
}
|
||||
|
||||
/// A pose track -- aggregate root for tracking one person.
|
||||
///
|
||||
/// Contains 17 keypoint Kalman states, lifecycle, and re-ID embedding.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PoseTrack {
|
||||
/// Unique track identifier.
|
||||
pub id: TrackId,
|
||||
/// Per-keypoint Kalman state (COCO-17 ordering).
|
||||
pub keypoints: [KeypointState; NUM_KEYPOINTS],
|
||||
/// Track lifecycle state.
|
||||
pub lifecycle: TrackLifecycleState,
|
||||
/// Running-average AETHER embedding for re-ID (128-dim).
|
||||
pub embedding: Vec<f32>,
|
||||
/// Total frames since creation.
|
||||
pub age: u64,
|
||||
/// Frames since last successful measurement update.
|
||||
pub time_since_update: u64,
|
||||
/// Number of consecutive measurement updates (for birth gate).
|
||||
pub consecutive_hits: u64,
|
||||
/// Creation timestamp in microseconds.
|
||||
pub created_at: u64,
|
||||
/// Last update timestamp in microseconds.
|
||||
pub updated_at: u64,
|
||||
}
|
||||
|
||||
impl PoseTrack {
|
||||
/// Create a new tentative track from a detection.
|
||||
pub fn new(
|
||||
id: TrackId,
|
||||
keypoint_positions: &[[f32; 3]; NUM_KEYPOINTS],
|
||||
timestamp_us: u64,
|
||||
embedding_dim: usize,
|
||||
) -> Self {
|
||||
let keypoints = std::array::from_fn(|i| {
|
||||
let [x, y, z] = keypoint_positions[i];
|
||||
KeypointState::new(x, y, z)
|
||||
});
|
||||
|
||||
Self {
|
||||
id,
|
||||
keypoints,
|
||||
lifecycle: TrackLifecycleState::Tentative,
|
||||
embedding: vec![0.0; embedding_dim],
|
||||
age: 0,
|
||||
time_since_update: 0,
|
||||
consecutive_hits: 1,
|
||||
created_at: timestamp_us,
|
||||
updated_at: timestamp_us,
|
||||
}
|
||||
}
|
||||
|
||||
/// Predict all keypoints forward by dt seconds.
|
||||
pub fn predict(&mut self, dt: f32, process_noise: f32) {
|
||||
for kp in &mut self.keypoints {
|
||||
kp.predict(dt, process_noise);
|
||||
}
|
||||
self.age += 1;
|
||||
self.time_since_update += 1;
|
||||
}
|
||||
|
||||
/// Update all keypoints with new measurements.
|
||||
///
|
||||
/// Also updates lifecycle state transitions based on birth/loss gates.
|
||||
pub fn update_keypoints(
|
||||
&mut self,
|
||||
measurements: &[[f32; 3]; NUM_KEYPOINTS],
|
||||
measurement_noise: f32,
|
||||
noise_multiplier: f32,
|
||||
timestamp_us: u64,
|
||||
) {
|
||||
for (kp, meas) in self.keypoints.iter_mut().zip(measurements.iter()) {
|
||||
kp.update(meas, measurement_noise, noise_multiplier);
|
||||
}
|
||||
|
||||
self.time_since_update = 0;
|
||||
self.consecutive_hits += 1;
|
||||
self.updated_at = timestamp_us;
|
||||
|
||||
// Lifecycle transitions
|
||||
self.update_lifecycle();
|
||||
}
|
||||
|
||||
/// Update the embedding with EMA decay.
|
||||
pub fn update_embedding(&mut self, new_embedding: &[f32], decay: f32) {
|
||||
if new_embedding.len() != self.embedding.len() {
|
||||
return;
|
||||
}
|
||||
|
||||
let alpha = 1.0 - decay;
|
||||
for (e, &ne) in self.embedding.iter_mut().zip(new_embedding.iter()) {
|
||||
*e = decay * *e + alpha * ne;
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute the centroid position (mean of all keypoints).
|
||||
pub fn centroid(&self) -> [f32; 3] {
|
||||
let n = NUM_KEYPOINTS as f32;
|
||||
let mut c = [0.0_f32; 3];
|
||||
for kp in &self.keypoints {
|
||||
let pos = kp.position();
|
||||
c[0] += pos[0];
|
||||
c[1] += pos[1];
|
||||
c[2] += pos[2];
|
||||
}
|
||||
c[0] /= n;
|
||||
c[1] /= n;
|
||||
c[2] /= n;
|
||||
c
|
||||
}
|
||||
|
||||
/// Compute torso jitter RMS in meters.
|
||||
///
|
||||
/// Uses the torso keypoints (shoulders, hips) velocity magnitudes
|
||||
/// as a proxy for jitter.
|
||||
pub fn torso_jitter_rms(&self) -> f32 {
|
||||
let torso_indices = super::keypoint::TORSO_INDICES;
|
||||
let mut sum_sq = 0.0_f32;
|
||||
let mut count = 0;
|
||||
|
||||
for &idx in torso_indices {
|
||||
let vel = self.keypoints[idx].velocity();
|
||||
let speed_sq = vel[0] * vel[0] + vel[1] * vel[1] + vel[2] * vel[2];
|
||||
sum_sq += speed_sq;
|
||||
count += 1;
|
||||
}
|
||||
|
||||
if count == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
(sum_sq / count as f32).sqrt()
|
||||
}
|
||||
|
||||
/// Mark the track as lost.
|
||||
pub fn mark_lost(&mut self) {
|
||||
if self.lifecycle != TrackLifecycleState::Terminated {
|
||||
self.lifecycle = TrackLifecycleState::Lost;
|
||||
}
|
||||
}
|
||||
|
||||
/// Mark the track as terminated.
|
||||
pub fn terminate(&mut self) {
|
||||
self.lifecycle = TrackLifecycleState::Terminated;
|
||||
}
|
||||
|
||||
/// Update lifecycle state based on consecutive hits and misses.
|
||||
fn update_lifecycle(&mut self) {
|
||||
match self.lifecycle {
|
||||
TrackLifecycleState::Tentative => {
|
||||
if self.consecutive_hits >= 2 {
|
||||
// Birth gate: promote to Active after 2 consecutive updates
|
||||
self.lifecycle = TrackLifecycleState::Active;
|
||||
}
|
||||
}
|
||||
TrackLifecycleState::Lost => {
|
||||
// Re-acquired: promote back to Active
|
||||
self.lifecycle = TrackLifecycleState::Active;
|
||||
self.consecutive_hits = 1;
|
||||
}
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Tracker configuration parameters.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TrackerConfig {
|
||||
/// Process noise acceleration (m/s^2). Default: 0.3.
|
||||
pub process_noise: f32,
|
||||
/// Measurement noise std dev (m). Default: 0.08.
|
||||
pub measurement_noise: f32,
|
||||
/// Mahalanobis gate threshold (chi-squared(3) at 3-sigma = 9.0).
|
||||
pub mahalanobis_gate: f32,
|
||||
/// Frames required for tentative->active promotion. Default: 2.
|
||||
pub birth_hits: u64,
|
||||
/// Max frames without update before tentative->lost. Default: 5.
|
||||
pub loss_misses: u64,
|
||||
/// Re-ID window in frames (5 seconds at 20Hz = 100). Default: 100.
|
||||
pub reid_window: u64,
|
||||
/// Embedding EMA decay rate. Default: 0.95.
|
||||
pub embedding_decay: f32,
|
||||
/// Embedding dimension. Default: 128.
|
||||
pub embedding_dim: usize,
|
||||
/// Position weight in assignment cost. Default: 0.6.
|
||||
pub position_weight: f32,
|
||||
/// Embedding weight in assignment cost. Default: 0.4.
|
||||
pub embedding_weight: f32,
|
||||
}
|
||||
|
||||
impl Default for TrackerConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
process_noise: 0.3,
|
||||
measurement_noise: 0.08,
|
||||
mahalanobis_gate: 9.0,
|
||||
birth_hits: 2,
|
||||
loss_misses: 5,
|
||||
reid_window: 100,
|
||||
embedding_decay: 0.95,
|
||||
embedding_dim: 128,
|
||||
position_weight: 0.6,
|
||||
embedding_weight: 0.4,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Multi-person pose tracker.
|
||||
///
|
||||
/// Manages a collection of `PoseTrack` instances with automatic lifecycle
|
||||
/// management, detection-to-track assignment, and re-identification.
|
||||
#[derive(Debug)]
|
||||
pub struct PoseTracker {
|
||||
config: TrackerConfig,
|
||||
tracks: Vec<PoseTrack>,
|
||||
next_id: u64,
|
||||
}
|
||||
|
||||
impl PoseTracker {
|
||||
/// Create a new tracker with default configuration.
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
config: TrackerConfig::default(),
|
||||
tracks: Vec::new(),
|
||||
next_id: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a new tracker with custom configuration.
|
||||
pub fn with_config(config: TrackerConfig) -> Self {
|
||||
Self {
|
||||
config,
|
||||
tracks: Vec::new(),
|
||||
next_id: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Return all active tracks (not terminated).
|
||||
pub fn active_tracks(&self) -> Vec<&PoseTrack> {
|
||||
self.tracks
|
||||
.iter()
|
||||
.filter(|t| t.lifecycle.is_alive())
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Return all tracks including terminated ones.
|
||||
pub fn all_tracks(&self) -> &[PoseTrack] {
|
||||
&self.tracks
|
||||
}
|
||||
|
||||
/// Return the number of active (alive) tracks.
|
||||
pub fn active_count(&self) -> usize {
|
||||
self.tracks.iter().filter(|t| t.lifecycle.is_alive()).count()
|
||||
}
|
||||
|
||||
/// Predict step for all tracks (advance by dt seconds).
|
||||
pub fn predict_all(&mut self, dt: f32) {
|
||||
for track in &mut self.tracks {
|
||||
if track.lifecycle.is_alive() {
|
||||
track.predict(dt, self.config.process_noise);
|
||||
}
|
||||
}
|
||||
|
||||
// Mark tracks as lost after exceeding loss_misses
|
||||
for track in &mut self.tracks {
|
||||
if track.lifecycle.accepts_updates()
|
||||
&& track.time_since_update >= self.config.loss_misses
|
||||
{
|
||||
track.mark_lost();
|
||||
}
|
||||
}
|
||||
|
||||
// Terminate tracks that have been lost too long
|
||||
let reid_window = self.config.reid_window;
|
||||
for track in &mut self.tracks {
|
||||
if track.lifecycle.is_lost() && track.time_since_update >= reid_window {
|
||||
track.terminate();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a new track from a detection.
|
||||
pub fn create_track(
|
||||
&mut self,
|
||||
keypoints: &[[f32; 3]; NUM_KEYPOINTS],
|
||||
timestamp_us: u64,
|
||||
) -> TrackId {
|
||||
let id = TrackId::new(self.next_id);
|
||||
self.next_id += 1;
|
||||
|
||||
let track = PoseTrack::new(id, keypoints, timestamp_us, self.config.embedding_dim);
|
||||
self.tracks.push(track);
|
||||
id
|
||||
}
|
||||
|
||||
/// Find the track with the given ID.
|
||||
pub fn find_track(&self, id: TrackId) -> Option<&PoseTrack> {
|
||||
self.tracks.iter().find(|t| t.id == id)
|
||||
}
|
||||
|
||||
/// Find the track with the given ID (mutable).
|
||||
pub fn find_track_mut(&mut self, id: TrackId) -> Option<&mut PoseTrack> {
|
||||
self.tracks.iter_mut().find(|t| t.id == id)
|
||||
}
|
||||
|
||||
/// Remove terminated tracks from the collection.
|
||||
pub fn prune_terminated(&mut self) {
|
||||
self.tracks
|
||||
.retain(|t| t.lifecycle != TrackLifecycleState::Terminated);
|
||||
}
|
||||
|
||||
/// Compute the assignment cost between a track and a detection.
|
||||
///
|
||||
/// cost = position_weight * mahalanobis(track, detection.position)
|
||||
/// + embedding_weight * (1 - cosine_sim(track.embedding, detection.embedding))
|
||||
pub fn assignment_cost(
|
||||
&self,
|
||||
track: &PoseTrack,
|
||||
detection_centroid: &[f32; 3],
|
||||
detection_embedding: &[f32],
|
||||
) -> f32 {
|
||||
// Position cost: Mahalanobis distance at centroid
|
||||
let centroid_kp = track.centroid();
|
||||
let centroid_state = KeypointState::new(centroid_kp[0], centroid_kp[1], centroid_kp[2]);
|
||||
let maha = centroid_state.mahalanobis_distance(detection_centroid);
|
||||
|
||||
// Embedding cost: 1 - cosine similarity
|
||||
let embed_cost = 1.0 - cosine_similarity(&track.embedding, detection_embedding);
|
||||
|
||||
self.config.position_weight * maha + self.config.embedding_weight * embed_cost
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for PoseTracker {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
/// Cosine similarity between two vectors.
|
||||
///
|
||||
/// Returns a value in [-1.0, 1.0] where 1.0 means identical direction.
|
||||
pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
|
||||
let n = a.len().min(b.len());
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let mut dot = 0.0_f32;
|
||||
let mut norm_a = 0.0_f32;
|
||||
let mut norm_b = 0.0_f32;
|
||||
|
||||
for i in 0..n {
|
||||
dot += a[i] * b[i];
|
||||
norm_a += a[i] * a[i];
|
||||
norm_b += b[i] * b[i];
|
||||
}
|
||||
|
||||
let denom = (norm_a * norm_b).sqrt();
|
||||
if denom < 1e-12 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
(dot / denom).clamp(-1.0, 1.0)
|
||||
}
|
||||
|
||||
/// A detected pose from the model, before assignment to a track.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PoseDetection {
|
||||
/// Per-keypoint positions [x, y, z, confidence] for 17 keypoints.
|
||||
pub keypoints: [[f32; 4]; NUM_KEYPOINTS],
|
||||
/// AETHER re-ID embedding (128-dim).
|
||||
pub embedding: Vec<f32>,
|
||||
}
|
||||
|
||||
impl PoseDetection {
|
||||
/// Extract the 3D position array from keypoints.
|
||||
pub fn positions(&self) -> [[f32; 3]; NUM_KEYPOINTS] {
|
||||
std::array::from_fn(|i| [self.keypoints[i][0], self.keypoints[i][1], self.keypoints[i][2]])
|
||||
}
|
||||
|
||||
/// Compute the centroid of the detection.
|
||||
pub fn centroid(&self) -> [f32; 3] {
|
||||
let n = NUM_KEYPOINTS as f32;
|
||||
let mut c = [0.0_f32; 3];
|
||||
for kp in &self.keypoints {
|
||||
c[0] += kp[0];
|
||||
c[1] += kp[1];
|
||||
c[2] += kp[2];
|
||||
}
|
||||
c[0] /= n;
|
||||
c[1] /= n;
|
||||
c[2] /= n;
|
||||
c
|
||||
}
|
||||
|
||||
/// Mean confidence across all keypoints.
|
||||
pub fn mean_confidence(&self) -> f32 {
|
||||
let sum: f32 = self.keypoints.iter().map(|kp| kp[3]).sum();
|
||||
sum / NUM_KEYPOINTS as f32
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn zero_positions() -> [[f32; 3]; NUM_KEYPOINTS] {
|
||||
[[0.0, 0.0, 0.0]; NUM_KEYPOINTS]
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
fn offset_positions(offset: f32) -> [[f32; 3]; NUM_KEYPOINTS] {
|
||||
std::array::from_fn(|i| [offset + i as f32 * 0.1, offset, 0.0])
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn keypoint_state_creation() {
|
||||
let kp = KeypointState::new(1.0, 2.0, 3.0);
|
||||
assert_eq!(kp.position(), [1.0, 2.0, 3.0]);
|
||||
assert_eq!(kp.velocity(), [0.0, 0.0, 0.0]);
|
||||
assert_eq!(kp.confidence, 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn keypoint_predict_moves_position() {
|
||||
let mut kp = KeypointState::new(0.0, 0.0, 0.0);
|
||||
kp.state[3] = 1.0; // vx = 1 m/s
|
||||
kp.predict(0.05, 0.3); // 50ms step
|
||||
assert!((kp.state[0] - 0.05).abs() < 1e-5, "x should be ~0.05, got {}", kp.state[0]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn keypoint_predict_increases_uncertainty() {
|
||||
let mut kp = KeypointState::new(0.0, 0.0, 0.0);
|
||||
let initial_var = kp.covariance[0];
|
||||
kp.predict(0.05, 0.3);
|
||||
assert!(kp.covariance[0] > initial_var);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn keypoint_update_reduces_uncertainty() {
|
||||
let mut kp = KeypointState::new(0.0, 0.0, 0.0);
|
||||
kp.predict(0.05, 0.3);
|
||||
let post_predict_var = kp.covariance[0];
|
||||
kp.update(&[0.01, 0.0, 0.0], 0.08, 1.0);
|
||||
assert!(kp.covariance[0] < post_predict_var);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mahalanobis_zero_distance() {
|
||||
let kp = KeypointState::new(1.0, 2.0, 3.0);
|
||||
let d = kp.mahalanobis_distance(&[1.0, 2.0, 3.0]);
|
||||
assert!(d < 1e-3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mahalanobis_positive_for_offset() {
|
||||
let kp = KeypointState::new(0.0, 0.0, 0.0);
|
||||
let d = kp.mahalanobis_distance(&[1.0, 0.0, 0.0]);
|
||||
assert!(d > 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn lifecycle_transitions() {
|
||||
assert!(TrackLifecycleState::Tentative.is_alive());
|
||||
assert!(TrackLifecycleState::Active.is_alive());
|
||||
assert!(TrackLifecycleState::Lost.is_alive());
|
||||
assert!(!TrackLifecycleState::Terminated.is_alive());
|
||||
|
||||
assert!(TrackLifecycleState::Tentative.accepts_updates());
|
||||
assert!(TrackLifecycleState::Active.accepts_updates());
|
||||
assert!(!TrackLifecycleState::Lost.accepts_updates());
|
||||
assert!(!TrackLifecycleState::Terminated.accepts_updates());
|
||||
|
||||
assert!(!TrackLifecycleState::Tentative.is_lost());
|
||||
assert!(TrackLifecycleState::Lost.is_lost());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_creation() {
|
||||
let positions = zero_positions();
|
||||
let track = PoseTrack::new(TrackId(0), &positions, 1000, 128);
|
||||
assert_eq!(track.id, TrackId(0));
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Tentative);
|
||||
assert_eq!(track.embedding.len(), 128);
|
||||
assert_eq!(track.age, 0);
|
||||
assert_eq!(track.consecutive_hits, 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_birth_gate() {
|
||||
let positions = zero_positions();
|
||||
let mut track = PoseTrack::new(TrackId(0), &positions, 0, 128);
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Tentative);
|
||||
|
||||
// First update: still tentative (need 2 hits)
|
||||
track.update_keypoints(&positions, 0.08, 1.0, 100);
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Active);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_loss_gate() {
|
||||
let positions = zero_positions();
|
||||
let mut track = PoseTrack::new(TrackId(0), &positions, 0, 128);
|
||||
track.lifecycle = TrackLifecycleState::Active;
|
||||
|
||||
// Predict without updates exceeding loss_misses
|
||||
for _ in 0..6 {
|
||||
track.predict(0.05, 0.3);
|
||||
}
|
||||
// Manually mark lost (normally done by tracker)
|
||||
if track.time_since_update >= 5 {
|
||||
track.mark_lost();
|
||||
}
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Lost);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_centroid() {
|
||||
let positions: [[f32; 3]; NUM_KEYPOINTS] =
|
||||
std::array::from_fn(|_| [1.0, 2.0, 3.0]);
|
||||
let track = PoseTrack::new(TrackId(0), &positions, 0, 128);
|
||||
let c = track.centroid();
|
||||
assert!((c[0] - 1.0).abs() < 1e-5);
|
||||
assert!((c[1] - 2.0).abs() < 1e-5);
|
||||
assert!((c[2] - 3.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_embedding_update() {
|
||||
let positions = zero_positions();
|
||||
let mut track = PoseTrack::new(TrackId(0), &positions, 0, 4);
|
||||
let new_embed = vec![1.0, 2.0, 3.0, 4.0];
|
||||
track.update_embedding(&new_embed, 0.5);
|
||||
// EMA: 0.5 * 0.0 + 0.5 * new = new / 2
|
||||
for i in 0..4 {
|
||||
assert!((track.embedding[i] - new_embed[i] * 0.5).abs() < 1e-5);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracker_create_and_find() {
|
||||
let mut tracker = PoseTracker::new();
|
||||
let positions = zero_positions();
|
||||
let id = tracker.create_track(&positions, 1000);
|
||||
assert!(tracker.find_track(id).is_some());
|
||||
assert_eq!(tracker.active_count(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracker_predict_marks_lost() {
|
||||
let mut tracker = PoseTracker::with_config(TrackerConfig {
|
||||
loss_misses: 3,
|
||||
reid_window: 10,
|
||||
..Default::default()
|
||||
});
|
||||
let positions = zero_positions();
|
||||
let id = tracker.create_track(&positions, 0);
|
||||
|
||||
// Promote to active
|
||||
if let Some(t) = tracker.find_track_mut(id) {
|
||||
t.lifecycle = TrackLifecycleState::Active;
|
||||
}
|
||||
|
||||
// Predict 4 times without update
|
||||
for _ in 0..4 {
|
||||
tracker.predict_all(0.05);
|
||||
}
|
||||
|
||||
let track = tracker.find_track(id).unwrap();
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Lost);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracker_prune_terminated() {
|
||||
let mut tracker = PoseTracker::new();
|
||||
let positions = zero_positions();
|
||||
let id = tracker.create_track(&positions, 0);
|
||||
if let Some(t) = tracker.find_track_mut(id) {
|
||||
t.terminate();
|
||||
}
|
||||
assert_eq!(tracker.all_tracks().len(), 1);
|
||||
tracker.prune_terminated();
|
||||
assert_eq!(tracker.all_tracks().len(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn cosine_similarity_identical() {
|
||||
let a = vec![1.0, 2.0, 3.0];
|
||||
let b = vec![1.0, 2.0, 3.0];
|
||||
assert!((cosine_similarity(&a, &b) - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn cosine_similarity_orthogonal() {
|
||||
let a = vec![1.0, 0.0, 0.0];
|
||||
let b = vec![0.0, 1.0, 0.0];
|
||||
assert!(cosine_similarity(&a, &b).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn cosine_similarity_opposite() {
|
||||
let a = vec![1.0, 2.0, 3.0];
|
||||
let b = vec![-1.0, -2.0, -3.0];
|
||||
assert!((cosine_similarity(&a, &b) + 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn cosine_similarity_empty() {
|
||||
assert_eq!(cosine_similarity(&[], &[]), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pose_detection_centroid() {
|
||||
let kps: [[f32; 4]; NUM_KEYPOINTS] =
|
||||
std::array::from_fn(|_| [1.0, 2.0, 3.0, 0.9]);
|
||||
let det = PoseDetection {
|
||||
keypoints: kps,
|
||||
embedding: vec![0.0; 128],
|
||||
};
|
||||
let c = det.centroid();
|
||||
assert!((c[0] - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pose_detection_mean_confidence() {
|
||||
let kps: [[f32; 4]; NUM_KEYPOINTS] =
|
||||
std::array::from_fn(|_| [0.0, 0.0, 0.0, 0.8]);
|
||||
let det = PoseDetection {
|
||||
keypoints: kps,
|
||||
embedding: vec![0.0; 128],
|
||||
};
|
||||
assert!((det.mean_confidence() - 0.8).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pose_detection_positions() {
|
||||
let kps: [[f32; 4]; NUM_KEYPOINTS] =
|
||||
std::array::from_fn(|i| [i as f32, 0.0, 0.0, 1.0]);
|
||||
let det = PoseDetection {
|
||||
keypoints: kps,
|
||||
embedding: vec![],
|
||||
};
|
||||
let pos = det.positions();
|
||||
assert_eq!(pos[0], [0.0, 0.0, 0.0]);
|
||||
assert_eq!(pos[5], [5.0, 0.0, 0.0]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn assignment_cost_computation() {
|
||||
let mut tracker = PoseTracker::new();
|
||||
let positions = zero_positions();
|
||||
let id = tracker.create_track(&positions, 0);
|
||||
|
||||
let track = tracker.find_track(id).unwrap();
|
||||
let cost = tracker.assignment_cost(track, &[0.0, 0.0, 0.0], &vec![0.0; 128]);
|
||||
// Zero distance + zero embedding cost should be near 0
|
||||
// But embedding cost = 1 - cosine_sim(zeros, zeros) = 1 - 0 = 1
|
||||
// So cost = 0.6 * 0 + 0.4 * 1 = 0.4
|
||||
assert!((cost - 0.4).abs() < 0.1, "Expected ~0.4, got {}", cost);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn torso_jitter_rms_stationary() {
|
||||
let positions = zero_positions();
|
||||
let track = PoseTrack::new(TrackId(0), &positions, 0, 128);
|
||||
let jitter = track.torso_jitter_rms();
|
||||
assert!(jitter < 1e-5, "Stationary track should have near-zero jitter");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn default_tracker_config() {
|
||||
let cfg = TrackerConfig::default();
|
||||
assert!((cfg.process_noise - 0.3).abs() < f32::EPSILON);
|
||||
assert!((cfg.measurement_noise - 0.08).abs() < f32::EPSILON);
|
||||
assert!((cfg.mahalanobis_gate - 9.0).abs() < f32::EPSILON);
|
||||
assert_eq!(cfg.birth_hits, 2);
|
||||
assert_eq!(cfg.loss_misses, 5);
|
||||
assert_eq!(cfg.reid_window, 100);
|
||||
assert!((cfg.embedding_decay - 0.95).abs() < f32::EPSILON);
|
||||
assert_eq!(cfg.embedding_dim, 128);
|
||||
assert!((cfg.position_weight - 0.6).abs() < f32::EPSILON);
|
||||
assert!((cfg.embedding_weight - 0.4).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn track_terminate_prevents_lost() {
|
||||
let positions = zero_positions();
|
||||
let mut track = PoseTrack::new(TrackId(0), &positions, 0, 128);
|
||||
track.terminate();
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Terminated);
|
||||
track.mark_lost(); // Should not override Terminated
|
||||
assert_eq!(track.lifecycle, TrackLifecycleState::Terminated);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,676 @@
|
||||
//! Coarse RF Tomography from link attenuations.
|
||||
//!
|
||||
//! Produces a low-resolution 3D occupancy volume by inverting per-link
|
||||
//! attenuation measurements. Each voxel receives an occupancy probability
|
||||
//! based on how many links traverse it and how much attenuation those links
|
||||
//! observed.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//! 1. Define a voxel grid covering the monitored volume
|
||||
//! 2. For each link, determine which voxels lie along the propagation path
|
||||
//! 3. Solve the sparse tomographic inverse: attenuation = sum(voxel_density * path_weight)
|
||||
//! 4. Apply L1 regularization for sparsity (most voxels are unoccupied)
|
||||
//!
|
||||
//! # References
|
||||
//! - ADR-030 Tier 2: Coarse RF Tomography
|
||||
//! - Wilson & Patwari (2010), "Radio Tomographic Imaging"
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Error types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Errors from tomography operations.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum TomographyError {
|
||||
/// Not enough links for tomographic inversion.
|
||||
#[error("Insufficient links: need >= {needed}, got {got}")]
|
||||
InsufficientLinks { needed: usize, got: usize },
|
||||
|
||||
/// Grid dimensions are invalid.
|
||||
#[error("Invalid grid dimensions: {0}")]
|
||||
InvalidGrid(String),
|
||||
|
||||
/// No voxels intersected by any link.
|
||||
#[error("No voxels intersected by links — check geometry")]
|
||||
NoIntersections,
|
||||
|
||||
/// Observation vector length mismatch.
|
||||
#[error("Observation length mismatch: expected {expected}, got {got}")]
|
||||
ObservationMismatch { expected: usize, got: usize },
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Configuration
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Configuration for the voxel grid and tomographic solver.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TomographyConfig {
|
||||
/// Number of voxels along X axis.
|
||||
pub nx: usize,
|
||||
/// Number of voxels along Y axis.
|
||||
pub ny: usize,
|
||||
/// Number of voxels along Z axis.
|
||||
pub nz: usize,
|
||||
/// Physical extent of the grid: `[x_min, y_min, z_min, x_max, y_max, z_max]`.
|
||||
pub bounds: [f64; 6],
|
||||
/// L1 regularization weight (higher = sparser solution).
|
||||
pub lambda: f64,
|
||||
/// Maximum iterations for the solver.
|
||||
pub max_iterations: usize,
|
||||
/// Convergence tolerance.
|
||||
pub tolerance: f64,
|
||||
/// Minimum links required for inversion (default 8).
|
||||
pub min_links: usize,
|
||||
}
|
||||
|
||||
impl Default for TomographyConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
nx: 8,
|
||||
ny: 8,
|
||||
nz: 4,
|
||||
bounds: [0.0, 0.0, 0.0, 6.0, 6.0, 3.0],
|
||||
lambda: 0.1,
|
||||
max_iterations: 100,
|
||||
tolerance: 1e-4,
|
||||
min_links: 8,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Geometry types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// A 3D position.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct Position3D {
|
||||
pub x: f64,
|
||||
pub y: f64,
|
||||
pub z: f64,
|
||||
}
|
||||
|
||||
/// A link between a transmitter and receiver.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct LinkGeometry {
|
||||
/// Transmitter position.
|
||||
pub tx: Position3D,
|
||||
/// Receiver position.
|
||||
pub rx: Position3D,
|
||||
/// Link identifier.
|
||||
pub link_id: usize,
|
||||
}
|
||||
|
||||
impl LinkGeometry {
|
||||
/// Euclidean distance between TX and RX.
|
||||
pub fn distance(&self) -> f64 {
|
||||
let dx = self.rx.x - self.tx.x;
|
||||
let dy = self.rx.y - self.tx.y;
|
||||
let dz = self.rx.z - self.tx.z;
|
||||
(dx * dx + dy * dy + dz * dz).sqrt()
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Occupancy volume
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// 3D occupancy grid resulting from tomographic inversion.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct OccupancyVolume {
|
||||
/// Voxel densities in row-major order `[nz][ny][nx]`.
|
||||
pub densities: Vec<f64>,
|
||||
/// Grid dimensions.
|
||||
pub nx: usize,
|
||||
pub ny: usize,
|
||||
pub nz: usize,
|
||||
/// Physical bounds.
|
||||
pub bounds: [f64; 6],
|
||||
/// Number of occupied voxels (density > threshold).
|
||||
pub occupied_count: usize,
|
||||
/// Total voxel count.
|
||||
pub total_voxels: usize,
|
||||
/// Solver residual at convergence.
|
||||
pub residual: f64,
|
||||
/// Number of iterations used.
|
||||
pub iterations: usize,
|
||||
}
|
||||
|
||||
impl OccupancyVolume {
|
||||
/// Get density at voxel (ix, iy, iz). Returns None if out of bounds.
|
||||
pub fn get(&self, ix: usize, iy: usize, iz: usize) -> Option<f64> {
|
||||
if ix < self.nx && iy < self.ny && iz < self.nz {
|
||||
Some(self.densities[iz * self.ny * self.nx + iy * self.nx + ix])
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
/// Voxel size along each axis.
|
||||
pub fn voxel_size(&self) -> [f64; 3] {
|
||||
[
|
||||
(self.bounds[3] - self.bounds[0]) / self.nx as f64,
|
||||
(self.bounds[4] - self.bounds[1]) / self.ny as f64,
|
||||
(self.bounds[5] - self.bounds[2]) / self.nz as f64,
|
||||
]
|
||||
}
|
||||
|
||||
/// Center position of voxel (ix, iy, iz).
|
||||
pub fn voxel_center(&self, ix: usize, iy: usize, iz: usize) -> Position3D {
|
||||
let vs = self.voxel_size();
|
||||
Position3D {
|
||||
x: self.bounds[0] + (ix as f64 + 0.5) * vs[0],
|
||||
y: self.bounds[1] + (iy as f64 + 0.5) * vs[1],
|
||||
z: self.bounds[2] + (iz as f64 + 0.5) * vs[2],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tomographic solver
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Coarse RF tomography solver.
|
||||
///
|
||||
/// Given a set of TX-RX links and per-link attenuation measurements,
|
||||
/// reconstructs a 3D occupancy volume using L1-regularized least squares.
|
||||
pub struct RfTomographer {
|
||||
config: TomographyConfig,
|
||||
/// Precomputed weight matrix: `weight_matrix[link_idx]` is a list of
|
||||
/// (voxel_index, weight) pairs.
|
||||
weight_matrix: Vec<Vec<(usize, f64)>>,
|
||||
/// Number of voxels.
|
||||
n_voxels: usize,
|
||||
}
|
||||
|
||||
impl RfTomographer {
|
||||
/// Create a new tomographer with the given configuration and link geometry.
|
||||
pub fn new(config: TomographyConfig, links: &[LinkGeometry]) -> Result<Self, TomographyError> {
|
||||
if links.len() < config.min_links {
|
||||
return Err(TomographyError::InsufficientLinks {
|
||||
needed: config.min_links,
|
||||
got: links.len(),
|
||||
});
|
||||
}
|
||||
if config.nx == 0 || config.ny == 0 || config.nz == 0 {
|
||||
return Err(TomographyError::InvalidGrid(
|
||||
"Grid dimensions must be > 0".into(),
|
||||
));
|
||||
}
|
||||
|
||||
let n_voxels = config.nx * config.ny * config.nz;
|
||||
|
||||
// Precompute weight matrix
|
||||
let weight_matrix: Vec<Vec<(usize, f64)>> = links
|
||||
.iter()
|
||||
.map(|link| compute_link_weights(link, &config))
|
||||
.collect();
|
||||
|
||||
// Ensure at least one link intersects some voxels
|
||||
let total_weights: usize = weight_matrix.iter().map(|w| w.len()).sum();
|
||||
if total_weights == 0 {
|
||||
return Err(TomographyError::NoIntersections);
|
||||
}
|
||||
|
||||
Ok(Self {
|
||||
config,
|
||||
weight_matrix,
|
||||
n_voxels,
|
||||
})
|
||||
}
|
||||
|
||||
/// Reconstruct occupancy from per-link attenuation measurements.
|
||||
///
|
||||
/// `attenuations` has one entry per link (same order as links passed to `new`).
|
||||
/// Higher attenuation indicates more obstruction along the link path.
|
||||
pub fn reconstruct(&self, attenuations: &[f64]) -> Result<OccupancyVolume, TomographyError> {
|
||||
if attenuations.len() != self.weight_matrix.len() {
|
||||
return Err(TomographyError::ObservationMismatch {
|
||||
expected: self.weight_matrix.len(),
|
||||
got: attenuations.len(),
|
||||
});
|
||||
}
|
||||
|
||||
// ISTA (Iterative Shrinkage-Thresholding Algorithm) for L1 minimization
|
||||
// min ||Wx - y||^2 + lambda * ||x||_1
|
||||
let mut x = vec![0.0_f64; self.n_voxels];
|
||||
let n_links = attenuations.len();
|
||||
|
||||
// Estimate step size: 1 / (max eigenvalue of W^T W)
|
||||
// Approximate by max column norm squared
|
||||
let mut col_norms = vec![0.0_f64; self.n_voxels];
|
||||
for weights in &self.weight_matrix {
|
||||
for &(idx, w) in weights {
|
||||
col_norms[idx] += w * w;
|
||||
}
|
||||
}
|
||||
let max_col_norm = col_norms.iter().cloned().fold(0.0_f64, f64::max).max(1e-10);
|
||||
let step_size = 1.0 / max_col_norm;
|
||||
|
||||
let mut residual = 0.0_f64;
|
||||
let mut iterations = 0;
|
||||
|
||||
for iter in 0..self.config.max_iterations {
|
||||
// Compute gradient: W^T (Wx - y)
|
||||
let mut gradient = vec![0.0_f64; self.n_voxels];
|
||||
residual = 0.0;
|
||||
|
||||
for (link_idx, weights) in self.weight_matrix.iter().enumerate() {
|
||||
// Forward: Wx for this link
|
||||
let predicted: f64 = weights.iter().map(|&(idx, w)| w * x[idx]).sum();
|
||||
let diff = predicted - attenuations[link_idx];
|
||||
residual += diff * diff;
|
||||
|
||||
// Backward: accumulate gradient
|
||||
for &(idx, w) in weights {
|
||||
gradient[idx] += w * diff;
|
||||
}
|
||||
}
|
||||
|
||||
residual = (residual / n_links as f64).sqrt();
|
||||
|
||||
// Gradient step + soft thresholding (proximal L1)
|
||||
let mut max_change = 0.0_f64;
|
||||
for i in 0..self.n_voxels {
|
||||
let new_val = x[i] - step_size * gradient[i];
|
||||
// Soft thresholding
|
||||
let threshold = self.config.lambda * step_size;
|
||||
let shrunk = if new_val > threshold {
|
||||
new_val - threshold
|
||||
} else if new_val < -threshold {
|
||||
new_val + threshold
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
// Non-negativity constraint (density >= 0)
|
||||
let clamped = shrunk.max(0.0);
|
||||
max_change = max_change.max((clamped - x[i]).abs());
|
||||
x[i] = clamped;
|
||||
}
|
||||
|
||||
iterations = iter + 1;
|
||||
|
||||
if max_change < self.config.tolerance {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// Count occupied voxels (density > 0.01)
|
||||
let occupied_count = x.iter().filter(|&&d| d > 0.01).count();
|
||||
|
||||
Ok(OccupancyVolume {
|
||||
densities: x,
|
||||
nx: self.config.nx,
|
||||
ny: self.config.ny,
|
||||
nz: self.config.nz,
|
||||
bounds: self.config.bounds,
|
||||
occupied_count,
|
||||
total_voxels: self.n_voxels,
|
||||
residual,
|
||||
iterations,
|
||||
})
|
||||
}
|
||||
|
||||
/// Number of links in this tomographer.
|
||||
pub fn n_links(&self) -> usize {
|
||||
self.weight_matrix.len()
|
||||
}
|
||||
|
||||
/// Number of voxels in the grid.
|
||||
pub fn n_voxels(&self) -> usize {
|
||||
self.n_voxels
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Weight computation (simplified ray-voxel intersection)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Compute the intersection weights of a link with the voxel grid.
|
||||
///
|
||||
/// Uses a simplified approach: for each voxel, computes the minimum
|
||||
/// distance from the voxel center to the link ray. Voxels within
|
||||
/// one Fresnel zone receive weight proportional to closeness.
|
||||
fn compute_link_weights(link: &LinkGeometry, config: &TomographyConfig) -> Vec<(usize, f64)> {
|
||||
let vx = (config.bounds[3] - config.bounds[0]) / config.nx as f64;
|
||||
let vy = (config.bounds[4] - config.bounds[1]) / config.ny as f64;
|
||||
let vz = (config.bounds[5] - config.bounds[2]) / config.nz as f64;
|
||||
|
||||
// Fresnel zone half-width (approximate)
|
||||
let link_dist = link.distance();
|
||||
let wavelength = 0.06; // ~5 GHz
|
||||
let fresnel_radius = (wavelength * link_dist / 4.0).sqrt().max(vx.max(vy));
|
||||
|
||||
let dx = link.rx.x - link.tx.x;
|
||||
let dy = link.rx.y - link.tx.y;
|
||||
let dz = link.rx.z - link.tx.z;
|
||||
|
||||
let mut weights = Vec::new();
|
||||
|
||||
for iz in 0..config.nz {
|
||||
for iy in 0..config.ny {
|
||||
for ix in 0..config.nx {
|
||||
let cx = config.bounds[0] + (ix as f64 + 0.5) * vx;
|
||||
let cy = config.bounds[1] + (iy as f64 + 0.5) * vy;
|
||||
let cz = config.bounds[2] + (iz as f64 + 0.5) * vz;
|
||||
|
||||
// Point-to-line distance
|
||||
let dist = point_to_segment_distance(
|
||||
cx, cy, cz, link.tx.x, link.tx.y, link.tx.z, dx, dy, dz, link_dist,
|
||||
);
|
||||
|
||||
if dist < fresnel_radius {
|
||||
// Weight decays with distance from link ray
|
||||
let w = 1.0 - dist / fresnel_radius;
|
||||
let idx = iz * config.ny * config.nx + iy * config.nx + ix;
|
||||
weights.push((idx, w));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
weights
|
||||
}
|
||||
|
||||
/// Distance from point (px,py,pz) to line segment defined by start + t*dir
|
||||
/// where dir = (dx,dy,dz) and segment length = `seg_len`.
|
||||
fn point_to_segment_distance(
|
||||
px: f64,
|
||||
py: f64,
|
||||
pz: f64,
|
||||
sx: f64,
|
||||
sy: f64,
|
||||
sz: f64,
|
||||
dx: f64,
|
||||
dy: f64,
|
||||
dz: f64,
|
||||
seg_len: f64,
|
||||
) -> f64 {
|
||||
if seg_len < 1e-12 {
|
||||
return ((px - sx).powi(2) + (py - sy).powi(2) + (pz - sz).powi(2)).sqrt();
|
||||
}
|
||||
|
||||
// Project point onto line: t = dot(P-S, D) / |D|^2
|
||||
let t = ((px - sx) * dx + (py - sy) * dy + (pz - sz) * dz) / (seg_len * seg_len);
|
||||
let t_clamped = t.clamp(0.0, 1.0);
|
||||
|
||||
let closest_x = sx + t_clamped * dx;
|
||||
let closest_y = sy + t_clamped * dy;
|
||||
let closest_z = sz + t_clamped * dz;
|
||||
|
||||
((px - closest_x).powi(2) + (py - closest_y).powi(2) + (pz - closest_z).powi(2)).sqrt()
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_square_links() -> Vec<LinkGeometry> {
|
||||
// 4 nodes in a square at z=1.5, 12 directed links
|
||||
let nodes = [
|
||||
Position3D {
|
||||
x: 0.5,
|
||||
y: 0.5,
|
||||
z: 1.5,
|
||||
},
|
||||
Position3D {
|
||||
x: 5.5,
|
||||
y: 0.5,
|
||||
z: 1.5,
|
||||
},
|
||||
Position3D {
|
||||
x: 5.5,
|
||||
y: 5.5,
|
||||
z: 1.5,
|
||||
},
|
||||
Position3D {
|
||||
x: 0.5,
|
||||
y: 5.5,
|
||||
z: 1.5,
|
||||
},
|
||||
];
|
||||
let mut links = Vec::new();
|
||||
let mut id = 0;
|
||||
for i in 0..4 {
|
||||
for j in 0..4 {
|
||||
if i != j {
|
||||
links.push(LinkGeometry {
|
||||
tx: nodes[i],
|
||||
rx: nodes[j],
|
||||
link_id: id,
|
||||
});
|
||||
id += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
links
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_tomographer_creation() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
assert_eq!(tomo.n_links(), 12);
|
||||
assert_eq!(tomo.n_voxels(), 8 * 8 * 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_insufficient_links() {
|
||||
let links = vec![LinkGeometry {
|
||||
tx: Position3D {
|
||||
x: 0.0,
|
||||
y: 0.0,
|
||||
z: 0.0,
|
||||
},
|
||||
rx: Position3D {
|
||||
x: 1.0,
|
||||
y: 0.0,
|
||||
z: 0.0,
|
||||
},
|
||||
link_id: 0,
|
||||
}];
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(matches!(
|
||||
RfTomographer::new(config, &links),
|
||||
Err(TomographyError::InsufficientLinks { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_invalid_grid() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
nx: 0,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(matches!(
|
||||
RfTomographer::new(config, &links),
|
||||
Err(TomographyError::InvalidGrid(_))
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_zero_attenuation_empty_room() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
// Zero attenuation = empty room
|
||||
let attenuations = vec![0.0; tomo.n_links()];
|
||||
let volume = tomo.reconstruct(&attenuations).unwrap();
|
||||
|
||||
assert_eq!(volume.total_voxels, 8 * 8 * 4);
|
||||
// All densities should be zero or near zero
|
||||
assert!(
|
||||
volume.occupied_count == 0,
|
||||
"Empty room should have no occupied voxels, got {}",
|
||||
volume.occupied_count
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_nonzero_attenuation_produces_density() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
lambda: 0.01, // light regularization
|
||||
max_iterations: 200,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
// Non-zero attenuations = something is there
|
||||
let attenuations: Vec<f64> = (0..tomo.n_links()).map(|i| 0.5 + 0.1 * i as f64).collect();
|
||||
let volume = tomo.reconstruct(&attenuations).unwrap();
|
||||
|
||||
assert!(
|
||||
volume.occupied_count > 0,
|
||||
"Non-zero attenuation should produce occupied voxels"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_observation_mismatch() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
let attenuations = vec![0.1; 3]; // wrong count
|
||||
assert!(matches!(
|
||||
tomo.reconstruct(&attenuations),
|
||||
Err(TomographyError::ObservationMismatch { .. })
|
||||
));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_voxel_access() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
let attenuations = vec![0.0; tomo.n_links()];
|
||||
let volume = tomo.reconstruct(&attenuations).unwrap();
|
||||
|
||||
// Valid access
|
||||
assert!(volume.get(0, 0, 0).is_some());
|
||||
assert!(volume.get(7, 7, 3).is_some());
|
||||
// Out of bounds
|
||||
assert!(volume.get(8, 0, 0).is_none());
|
||||
assert!(volume.get(0, 8, 0).is_none());
|
||||
assert!(volume.get(0, 0, 4).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_voxel_center() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
nx: 6,
|
||||
ny: 6,
|
||||
nz: 3,
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
let attenuations = vec![0.0; tomo.n_links()];
|
||||
let volume = tomo.reconstruct(&attenuations).unwrap();
|
||||
|
||||
let center = volume.voxel_center(0, 0, 0);
|
||||
assert!(center.x > 0.0 && center.x < 1.0);
|
||||
assert!(center.y > 0.0 && center.y < 1.0);
|
||||
assert!(center.z > 0.0 && center.z < 1.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_voxel_size() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
nx: 6,
|
||||
ny: 6,
|
||||
nz: 3,
|
||||
bounds: [0.0, 0.0, 0.0, 6.0, 6.0, 3.0],
|
||||
min_links: 8,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
let attenuations = vec![0.0; tomo.n_links()];
|
||||
let volume = tomo.reconstruct(&attenuations).unwrap();
|
||||
let vs = volume.voxel_size();
|
||||
|
||||
assert!((vs[0] - 1.0).abs() < 1e-10);
|
||||
assert!((vs[1] - 1.0).abs() < 1e-10);
|
||||
assert!((vs[2] - 1.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_point_to_segment_distance() {
|
||||
// Point directly on the segment
|
||||
let d = point_to_segment_distance(0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0);
|
||||
assert!(d < 1e-10);
|
||||
|
||||
// Point 1 unit above the midpoint
|
||||
let d = point_to_segment_distance(0.5, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0);
|
||||
assert!((d - 1.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_link_distance() {
|
||||
let link = LinkGeometry {
|
||||
tx: Position3D {
|
||||
x: 0.0,
|
||||
y: 0.0,
|
||||
z: 0.0,
|
||||
},
|
||||
rx: Position3D {
|
||||
x: 3.0,
|
||||
y: 4.0,
|
||||
z: 0.0,
|
||||
},
|
||||
link_id: 0,
|
||||
};
|
||||
assert!((link.distance() - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_solver_convergence() {
|
||||
let links = make_square_links();
|
||||
let config = TomographyConfig {
|
||||
min_links: 8,
|
||||
lambda: 0.01,
|
||||
max_iterations: 500,
|
||||
tolerance: 1e-6,
|
||||
..Default::default()
|
||||
};
|
||||
let tomo = RfTomographer::new(config, &links).unwrap();
|
||||
|
||||
let attenuations: Vec<f64> = (0..tomo.n_links())
|
||||
.map(|i| 0.3 * (i as f64 * 0.7).sin().abs())
|
||||
.collect();
|
||||
let volume = tomo.reconstruct(&attenuations).unwrap();
|
||||
|
||||
assert!(volume.residual.is_finite());
|
||||
assert!(volume.iterations > 0);
|
||||
}
|
||||
}
|
||||
@@ -50,6 +50,7 @@ pub mod error;
|
||||
pub mod eval;
|
||||
pub mod geometry;
|
||||
pub mod rapid_adapt;
|
||||
pub mod ruview_metrics;
|
||||
pub mod subcarrier;
|
||||
pub mod virtual_aug;
|
||||
|
||||
|
||||
@@ -0,0 +1,945 @@
|
||||
//! RuView three-metric acceptance test (ADR-031).
|
||||
//!
|
||||
//! Implements the tiered pass/fail acceptance criteria for multistatic fusion:
|
||||
//!
|
||||
//! 1. **Joint Error (PCK / OKS)**: pose estimation accuracy.
|
||||
//! 2. **Multi-Person Separation (MOTA)**: tracking identity maintenance.
|
||||
//! 3. **Vital Sign Accuracy**: breathing and heartbeat detection precision.
|
||||
//!
|
||||
//! Tiered evaluation:
|
||||
//!
|
||||
//! | Tier | Requirements | Deployment Gate |
|
||||
//! |--------|----------------|------------------------|
|
||||
//! | Bronze | Metric 2 | Prototype demo |
|
||||
//! | Silver | Metrics 1 + 2 | Production candidate |
|
||||
//! | Gold | All three | Full deployment |
|
||||
//!
|
||||
//! # No mock data
|
||||
//!
|
||||
//! All computations use real metric definitions from the COCO evaluation
|
||||
//! protocol, MOT challenge MOTA definition, and signal-processing SNR
|
||||
//! measurement. No synthetic values are introduced at runtime.
|
||||
|
||||
use ndarray::{Array1, Array2};
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tier definitions
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Deployment tier achieved by the acceptance test.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
|
||||
pub enum RuViewTier {
|
||||
/// No tier met -- system fails acceptance.
|
||||
Fail,
|
||||
/// Metric 2 (tracking) passes. Prototype demo gate.
|
||||
Bronze,
|
||||
/// Metrics 1 + 2 (pose + tracking) pass. Production candidate gate.
|
||||
Silver,
|
||||
/// All three metrics pass. Full deployment gate.
|
||||
Gold,
|
||||
}
|
||||
|
||||
impl std::fmt::Display for RuViewTier {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
match self {
|
||||
RuViewTier::Fail => write!(f, "FAIL"),
|
||||
RuViewTier::Bronze => write!(f, "BRONZE"),
|
||||
RuViewTier::Silver => write!(f, "SILVER"),
|
||||
RuViewTier::Gold => write!(f, "GOLD"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Metric 1: Joint Error (PCK / OKS)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Thresholds for Metric 1 (Joint Error).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct JointErrorThresholds {
|
||||
/// PCK@0.2 all 17 keypoints (>= this to pass).
|
||||
pub pck_all: f32,
|
||||
/// PCK@0.2 torso keypoints (shoulders + hips, >= this to pass).
|
||||
pub pck_torso: f32,
|
||||
/// Mean OKS (>= this to pass).
|
||||
pub oks: f32,
|
||||
/// Torso jitter RMS in metres over 10s window (< this to pass).
|
||||
pub jitter_rms_m: f32,
|
||||
/// Per-keypoint max error 95th percentile in metres (< this to pass).
|
||||
pub max_error_p95_m: f32,
|
||||
}
|
||||
|
||||
impl Default for JointErrorThresholds {
|
||||
fn default() -> Self {
|
||||
JointErrorThresholds {
|
||||
pck_all: 0.70,
|
||||
pck_torso: 0.80,
|
||||
oks: 0.50,
|
||||
jitter_rms_m: 0.03,
|
||||
max_error_p95_m: 0.15,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Result of Metric 1 evaluation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct JointErrorResult {
|
||||
/// PCK@0.2 over all 17 keypoints.
|
||||
pub pck_all: f32,
|
||||
/// PCK@0.2 over torso keypoints (indices 5, 6, 11, 12).
|
||||
pub pck_torso: f32,
|
||||
/// Mean OKS.
|
||||
pub oks: f32,
|
||||
/// Torso jitter RMS (metres).
|
||||
pub jitter_rms_m: f32,
|
||||
/// Per-keypoint max error 95th percentile (metres).
|
||||
pub max_error_p95_m: f32,
|
||||
/// Whether this metric passes.
|
||||
pub passes: bool,
|
||||
}
|
||||
|
||||
/// COCO keypoint sigmas for OKS computation (17 joints).
|
||||
const COCO_SIGMAS: [f32; 17] = [
|
||||
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,
|
||||
0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089,
|
||||
];
|
||||
|
||||
/// Torso keypoint indices (COCO ordering): left_shoulder, right_shoulder,
|
||||
/// left_hip, right_hip.
|
||||
const TORSO_INDICES: [usize; 4] = [5, 6, 11, 12];
|
||||
|
||||
/// Evaluate Metric 1: Joint Error.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `pred_kpts`: per-frame predicted keypoints `[17, 2]` in normalised `[0,1]`.
|
||||
/// - `gt_kpts`: per-frame ground-truth keypoints `[17, 2]`.
|
||||
/// - `visibility`: per-frame visibility `[17]`, 0 = invisible.
|
||||
/// - `scale`: per-frame object scale for OKS (pass 1.0 if unknown).
|
||||
/// - `thresholds`: acceptance thresholds.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// `JointErrorResult` with the computed metrics and pass/fail.
|
||||
pub fn evaluate_joint_error(
|
||||
pred_kpts: &[Array2<f32>],
|
||||
gt_kpts: &[Array2<f32>],
|
||||
visibility: &[Array1<f32>],
|
||||
scale: &[f32],
|
||||
thresholds: &JointErrorThresholds,
|
||||
) -> JointErrorResult {
|
||||
let n = pred_kpts.len();
|
||||
if n == 0 {
|
||||
return JointErrorResult {
|
||||
pck_all: 0.0,
|
||||
pck_torso: 0.0,
|
||||
oks: 0.0,
|
||||
jitter_rms_m: f32::MAX,
|
||||
max_error_p95_m: f32::MAX,
|
||||
passes: false,
|
||||
};
|
||||
}
|
||||
|
||||
// PCK@0.2 computation.
|
||||
let pck_threshold = 0.2;
|
||||
let mut all_correct = 0_usize;
|
||||
let mut all_total = 0_usize;
|
||||
let mut torso_correct = 0_usize;
|
||||
let mut torso_total = 0_usize;
|
||||
let mut oks_sum = 0.0_f64;
|
||||
let mut per_kp_errors: Vec<Vec<f32>> = vec![Vec::new(); 17];
|
||||
|
||||
for i in 0..n {
|
||||
let bbox_diag = compute_bbox_diag(>_kpts[i], &visibility[i]);
|
||||
let safe_diag = bbox_diag.max(1e-3);
|
||||
let dist_thr = pck_threshold * safe_diag;
|
||||
|
||||
for j in 0..17 {
|
||||
if visibility[i][j] < 0.5 {
|
||||
continue;
|
||||
}
|
||||
let dx = pred_kpts[i][[j, 0]] - gt_kpts[i][[j, 0]];
|
||||
let dy = pred_kpts[i][[j, 1]] - gt_kpts[i][[j, 1]];
|
||||
let dist = (dx * dx + dy * dy).sqrt();
|
||||
|
||||
per_kp_errors[j].push(dist);
|
||||
|
||||
all_total += 1;
|
||||
if dist <= dist_thr {
|
||||
all_correct += 1;
|
||||
}
|
||||
|
||||
if TORSO_INDICES.contains(&j) {
|
||||
torso_total += 1;
|
||||
if dist <= dist_thr {
|
||||
torso_correct += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// OKS for this frame.
|
||||
let s = scale.get(i).copied().unwrap_or(1.0);
|
||||
let oks_frame = compute_single_oks(&pred_kpts[i], >_kpts[i], &visibility[i], s);
|
||||
oks_sum += oks_frame as f64;
|
||||
}
|
||||
|
||||
let pck_all = if all_total > 0 { all_correct as f32 / all_total as f32 } else { 0.0 };
|
||||
let pck_torso = if torso_total > 0 { torso_correct as f32 / torso_total as f32 } else { 0.0 };
|
||||
let oks = (oks_sum / n as f64) as f32;
|
||||
|
||||
// Torso jitter: RMS of frame-to-frame torso centroid displacement.
|
||||
let jitter_rms_m = compute_torso_jitter(pred_kpts, visibility);
|
||||
|
||||
// 95th percentile max per-keypoint error.
|
||||
let max_error_p95_m = compute_p95_max_error(&per_kp_errors);
|
||||
|
||||
let passes = pck_all >= thresholds.pck_all
|
||||
&& pck_torso >= thresholds.pck_torso
|
||||
&& oks >= thresholds.oks
|
||||
&& jitter_rms_m < thresholds.jitter_rms_m
|
||||
&& max_error_p95_m < thresholds.max_error_p95_m;
|
||||
|
||||
JointErrorResult {
|
||||
pck_all,
|
||||
pck_torso,
|
||||
oks,
|
||||
jitter_rms_m,
|
||||
max_error_p95_m,
|
||||
passes,
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Metric 2: Multi-Person Separation (MOTA)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Thresholds for Metric 2 (Multi-Person Separation).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TrackingThresholds {
|
||||
/// Maximum allowed identity switches (MOTA ID-switch). Must be 0 for pass.
|
||||
pub max_id_switches: usize,
|
||||
/// Maximum track fragmentation ratio (< this to pass).
|
||||
pub max_frag_ratio: f32,
|
||||
/// Maximum false track creations per minute (must be 0 for pass).
|
||||
pub max_false_tracks_per_min: f32,
|
||||
}
|
||||
|
||||
impl Default for TrackingThresholds {
|
||||
fn default() -> Self {
|
||||
TrackingThresholds {
|
||||
max_id_switches: 0,
|
||||
max_frag_ratio: 0.05,
|
||||
max_false_tracks_per_min: 0.0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A single frame of tracking data for MOTA computation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TrackingFrame {
|
||||
/// Frame index (0-based).
|
||||
pub frame_idx: usize,
|
||||
/// Ground-truth person IDs present in this frame.
|
||||
pub gt_ids: Vec<u32>,
|
||||
/// Predicted person IDs present in this frame.
|
||||
pub pred_ids: Vec<u32>,
|
||||
/// Assignment: `(pred_id, gt_id)` pairs for matched persons.
|
||||
pub assignments: Vec<(u32, u32)>,
|
||||
}
|
||||
|
||||
/// Result of Metric 2 evaluation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TrackingResult {
|
||||
/// Number of identity switches across the sequence.
|
||||
pub id_switches: usize,
|
||||
/// Track fragmentation ratio.
|
||||
pub fragmentation_ratio: f32,
|
||||
/// False track creations per minute.
|
||||
pub false_tracks_per_min: f32,
|
||||
/// MOTA score (higher is better).
|
||||
pub mota: f32,
|
||||
/// Total number of frames evaluated.
|
||||
pub n_frames: usize,
|
||||
/// Whether this metric passes.
|
||||
pub passes: bool,
|
||||
}
|
||||
|
||||
/// Evaluate Metric 2: Multi-Person Separation.
|
||||
///
|
||||
/// Computes MOTA (Multiple Object Tracking Accuracy) components:
|
||||
/// identity switches, fragmentation ratio, and false track rate.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `frames`: per-frame tracking data with GT and predicted IDs + assignments.
|
||||
/// - `duration_minutes`: total duration of the tracking sequence in minutes.
|
||||
/// - `thresholds`: acceptance thresholds.
|
||||
pub fn evaluate_tracking(
|
||||
frames: &[TrackingFrame],
|
||||
duration_minutes: f32,
|
||||
thresholds: &TrackingThresholds,
|
||||
) -> TrackingResult {
|
||||
let n_frames = frames.len();
|
||||
if n_frames == 0 {
|
||||
return TrackingResult {
|
||||
id_switches: 0,
|
||||
fragmentation_ratio: 0.0,
|
||||
false_tracks_per_min: 0.0,
|
||||
mota: 0.0,
|
||||
n_frames: 0,
|
||||
passes: false,
|
||||
};
|
||||
}
|
||||
|
||||
// Count identity switches: a switch occurs when the predicted ID assigned
|
||||
// to a GT ID changes between consecutive frames.
|
||||
let mut id_switches = 0_usize;
|
||||
let mut prev_assignment: std::collections::HashMap<u32, u32> = std::collections::HashMap::new();
|
||||
let mut total_gt = 0_usize;
|
||||
let mut total_misses = 0_usize;
|
||||
let mut total_false_positives = 0_usize;
|
||||
|
||||
// Track fragmentation: count how many times a GT track is "broken"
|
||||
// (present in one frame, absent in the next, then present again).
|
||||
let mut gt_track_presence: std::collections::HashMap<u32, Vec<bool>> =
|
||||
std::collections::HashMap::new();
|
||||
|
||||
for frame in frames {
|
||||
total_gt += frame.gt_ids.len();
|
||||
let n_matched = frame.assignments.len();
|
||||
total_misses += frame.gt_ids.len().saturating_sub(n_matched);
|
||||
total_false_positives += frame.pred_ids.len().saturating_sub(n_matched);
|
||||
|
||||
let mut current_assignment: std::collections::HashMap<u32, u32> =
|
||||
std::collections::HashMap::new();
|
||||
for &(pred_id, gt_id) in &frame.assignments {
|
||||
current_assignment.insert(gt_id, pred_id);
|
||||
if let Some(&prev_pred) = prev_assignment.get(>_id) {
|
||||
if prev_pred != pred_id {
|
||||
id_switches += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Track presence for fragmentation.
|
||||
for >_id in &frame.gt_ids {
|
||||
gt_track_presence
|
||||
.entry(gt_id)
|
||||
.or_default()
|
||||
.push(frame.assignments.iter().any(|&(_, gid)| gid == gt_id));
|
||||
}
|
||||
|
||||
prev_assignment = current_assignment;
|
||||
}
|
||||
|
||||
// Fragmentation ratio: fraction of GT tracks that have gaps.
|
||||
let mut n_fragmented = 0_usize;
|
||||
let mut n_tracks = 0_usize;
|
||||
for presence in gt_track_presence.values() {
|
||||
if presence.len() < 2 {
|
||||
continue;
|
||||
}
|
||||
n_tracks += 1;
|
||||
let mut has_gap = false;
|
||||
let mut was_present = false;
|
||||
let mut lost = false;
|
||||
for &present in presence {
|
||||
if was_present && !present {
|
||||
lost = true;
|
||||
}
|
||||
if lost && present {
|
||||
has_gap = true;
|
||||
break;
|
||||
}
|
||||
was_present = present;
|
||||
}
|
||||
if has_gap {
|
||||
n_fragmented += 1;
|
||||
}
|
||||
}
|
||||
|
||||
let fragmentation_ratio = if n_tracks > 0 {
|
||||
n_fragmented as f32 / n_tracks as f32
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
// False tracks per minute.
|
||||
let safe_duration = duration_minutes.max(1e-6);
|
||||
let false_tracks_per_min = total_false_positives as f32 / safe_duration;
|
||||
|
||||
// MOTA = 1 - (misses + false_positives + id_switches) / total_gt
|
||||
let mota = if total_gt > 0 {
|
||||
1.0 - (total_misses + total_false_positives + id_switches) as f32 / total_gt as f32
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
let passes = id_switches <= thresholds.max_id_switches
|
||||
&& fragmentation_ratio < thresholds.max_frag_ratio
|
||||
&& false_tracks_per_min <= thresholds.max_false_tracks_per_min;
|
||||
|
||||
TrackingResult {
|
||||
id_switches,
|
||||
fragmentation_ratio,
|
||||
false_tracks_per_min,
|
||||
mota,
|
||||
n_frames,
|
||||
passes,
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Metric 3: Vital Sign Accuracy
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Thresholds for Metric 3 (Vital Sign Accuracy).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct VitalSignThresholds {
|
||||
/// Breathing rate accuracy tolerance (BPM).
|
||||
pub breathing_bpm_tolerance: f32,
|
||||
/// Breathing band SNR minimum (dB).
|
||||
pub breathing_snr_db: f32,
|
||||
/// Heartbeat rate accuracy tolerance (BPM, aspirational).
|
||||
pub heartbeat_bpm_tolerance: f32,
|
||||
/// Heartbeat band SNR minimum (dB, aspirational).
|
||||
pub heartbeat_snr_db: f32,
|
||||
/// Micro-motion resolution in metres.
|
||||
pub micro_motion_m: f32,
|
||||
/// Range for micro-motion test (metres).
|
||||
pub micro_motion_range_m: f32,
|
||||
}
|
||||
|
||||
impl Default for VitalSignThresholds {
|
||||
fn default() -> Self {
|
||||
VitalSignThresholds {
|
||||
breathing_bpm_tolerance: 2.0,
|
||||
breathing_snr_db: 6.0,
|
||||
heartbeat_bpm_tolerance: 5.0,
|
||||
heartbeat_snr_db: 3.0,
|
||||
micro_motion_m: 0.001,
|
||||
micro_motion_range_m: 3.0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A single vital sign measurement for evaluation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct VitalSignMeasurement {
|
||||
/// Estimated breathing rate (BPM).
|
||||
pub breathing_bpm: f32,
|
||||
/// Ground-truth breathing rate (BPM).
|
||||
pub gt_breathing_bpm: f32,
|
||||
/// Breathing band SNR (dB).
|
||||
pub breathing_snr_db: f32,
|
||||
/// Estimated heartbeat rate (BPM), if available.
|
||||
pub heartbeat_bpm: Option<f32>,
|
||||
/// Ground-truth heartbeat rate (BPM), if available.
|
||||
pub gt_heartbeat_bpm: Option<f32>,
|
||||
/// Heartbeat band SNR (dB), if available.
|
||||
pub heartbeat_snr_db: Option<f32>,
|
||||
}
|
||||
|
||||
/// Result of Metric 3 evaluation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct VitalSignResult {
|
||||
/// Mean breathing rate error (BPM).
|
||||
pub breathing_error_bpm: f32,
|
||||
/// Mean breathing SNR (dB).
|
||||
pub breathing_snr_db: f32,
|
||||
/// Mean heartbeat rate error (BPM), if measured.
|
||||
pub heartbeat_error_bpm: Option<f32>,
|
||||
/// Mean heartbeat SNR (dB), if measured.
|
||||
pub heartbeat_snr_db: Option<f32>,
|
||||
/// Number of measurements evaluated.
|
||||
pub n_measurements: usize,
|
||||
/// Whether this metric passes.
|
||||
pub passes: bool,
|
||||
}
|
||||
|
||||
/// Evaluate Metric 3: Vital Sign Accuracy.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// - `measurements`: per-epoch vital sign measurements with GT.
|
||||
/// - `thresholds`: acceptance thresholds.
|
||||
pub fn evaluate_vital_signs(
|
||||
measurements: &[VitalSignMeasurement],
|
||||
thresholds: &VitalSignThresholds,
|
||||
) -> VitalSignResult {
|
||||
let n = measurements.len();
|
||||
if n == 0 {
|
||||
return VitalSignResult {
|
||||
breathing_error_bpm: f32::MAX,
|
||||
breathing_snr_db: 0.0,
|
||||
heartbeat_error_bpm: None,
|
||||
heartbeat_snr_db: None,
|
||||
n_measurements: 0,
|
||||
passes: false,
|
||||
};
|
||||
}
|
||||
|
||||
// Breathing metrics.
|
||||
let breathing_errors: Vec<f32> = measurements
|
||||
.iter()
|
||||
.map(|m| (m.breathing_bpm - m.gt_breathing_bpm).abs())
|
||||
.collect();
|
||||
let breathing_error_mean = breathing_errors.iter().sum::<f32>() / n as f32;
|
||||
let breathing_snr_mean =
|
||||
measurements.iter().map(|m| m.breathing_snr_db).sum::<f32>() / n as f32;
|
||||
|
||||
// Heartbeat metrics (optional).
|
||||
let heartbeat_pairs: Vec<(f32, f32, f32)> = measurements
|
||||
.iter()
|
||||
.filter_map(|m| {
|
||||
match (m.heartbeat_bpm, m.gt_heartbeat_bpm, m.heartbeat_snr_db) {
|
||||
(Some(hb), Some(gt), Some(snr)) => Some((hb, gt, snr)),
|
||||
_ => None,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
let (heartbeat_error, heartbeat_snr) = if heartbeat_pairs.is_empty() {
|
||||
(None, None)
|
||||
} else {
|
||||
let hb_n = heartbeat_pairs.len() as f32;
|
||||
let err = heartbeat_pairs
|
||||
.iter()
|
||||
.map(|(hb, gt, _)| (hb - gt).abs())
|
||||
.sum::<f32>()
|
||||
/ hb_n;
|
||||
let snr = heartbeat_pairs.iter().map(|(_, _, s)| s).sum::<f32>() / hb_n;
|
||||
(Some(err), Some(snr))
|
||||
};
|
||||
|
||||
// Pass/fail: breathing must pass; heartbeat is aspirational.
|
||||
let breathing_passes = breathing_error_mean <= thresholds.breathing_bpm_tolerance
|
||||
&& breathing_snr_mean >= thresholds.breathing_snr_db;
|
||||
|
||||
let heartbeat_passes = match (heartbeat_error, heartbeat_snr) {
|
||||
(Some(err), Some(snr)) => {
|
||||
err <= thresholds.heartbeat_bpm_tolerance && snr >= thresholds.heartbeat_snr_db
|
||||
}
|
||||
_ => true, // No heartbeat data: aspirational, not required.
|
||||
};
|
||||
|
||||
let passes = breathing_passes && heartbeat_passes;
|
||||
|
||||
VitalSignResult {
|
||||
breathing_error_bpm: breathing_error_mean,
|
||||
breathing_snr_db: breathing_snr_mean,
|
||||
heartbeat_error_bpm: heartbeat_error,
|
||||
heartbeat_snr_db: heartbeat_snr,
|
||||
n_measurements: n,
|
||||
passes,
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tiered acceptance
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Combined result of all three metrics with tier determination.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RuViewAcceptanceResult {
|
||||
/// Metric 1: Joint Error.
|
||||
pub joint_error: JointErrorResult,
|
||||
/// Metric 2: Tracking.
|
||||
pub tracking: TrackingResult,
|
||||
/// Metric 3: Vital Signs.
|
||||
pub vital_signs: VitalSignResult,
|
||||
/// Achieved deployment tier.
|
||||
pub tier: RuViewTier,
|
||||
}
|
||||
|
||||
impl RuViewAcceptanceResult {
|
||||
/// A human-readable summary of the acceptance test.
|
||||
pub fn summary(&self) -> String {
|
||||
format!(
|
||||
"RuView Tier={} | PCK={:.3} OKS={:.3} | MOTA={:.3} IDsw={} | Breathing={:.1}BPM err",
|
||||
self.tier,
|
||||
self.joint_error.pck_all,
|
||||
self.joint_error.oks,
|
||||
self.tracking.mota,
|
||||
self.tracking.id_switches,
|
||||
self.vital_signs.breathing_error_bpm,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/// Determine the deployment tier from individual metric results.
|
||||
pub fn determine_tier(
|
||||
joint_error: &JointErrorResult,
|
||||
tracking: &TrackingResult,
|
||||
vital_signs: &VitalSignResult,
|
||||
) -> RuViewTier {
|
||||
if !tracking.passes {
|
||||
return RuViewTier::Fail;
|
||||
}
|
||||
// Bronze: only tracking passes.
|
||||
if !joint_error.passes {
|
||||
return RuViewTier::Bronze;
|
||||
}
|
||||
// Silver: tracking + joint error pass.
|
||||
if !vital_signs.passes {
|
||||
return RuViewTier::Silver;
|
||||
}
|
||||
// Gold: all pass.
|
||||
RuViewTier::Gold
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Internal helpers
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
fn compute_bbox_diag(kp: &Array2<f32>, vis: &Array1<f32>) -> f32 {
|
||||
let mut x_min = f32::MAX;
|
||||
let mut x_max = f32::MIN;
|
||||
let mut y_min = f32::MAX;
|
||||
let mut y_max = f32::MIN;
|
||||
let mut any = false;
|
||||
|
||||
for j in 0..17.min(kp.shape()[0]) {
|
||||
if vis[j] >= 0.5 {
|
||||
let x = kp[[j, 0]];
|
||||
let y = kp[[j, 1]];
|
||||
x_min = x_min.min(x);
|
||||
x_max = x_max.max(x);
|
||||
y_min = y_min.min(y);
|
||||
y_max = y_max.max(y);
|
||||
any = true;
|
||||
}
|
||||
}
|
||||
if !any {
|
||||
return 0.0;
|
||||
}
|
||||
let w = (x_max - x_min).max(0.0);
|
||||
let h = (y_max - y_min).max(0.0);
|
||||
(w * w + h * h).sqrt()
|
||||
}
|
||||
|
||||
fn compute_single_oks(pred: &Array2<f32>, gt: &Array2<f32>, vis: &Array1<f32>, s: f32) -> f32 {
|
||||
let s_sq = s * s;
|
||||
let mut num = 0.0_f32;
|
||||
let mut den = 0.0_f32;
|
||||
for j in 0..17 {
|
||||
if vis[j] < 0.5 {
|
||||
continue;
|
||||
}
|
||||
den += 1.0;
|
||||
let dx = pred[[j, 0]] - gt[[j, 0]];
|
||||
let dy = pred[[j, 1]] - gt[[j, 1]];
|
||||
let d_sq = dx * dx + dy * dy;
|
||||
let k = COCO_SIGMAS[j];
|
||||
num += (-d_sq / (2.0 * s_sq * k * k)).exp();
|
||||
}
|
||||
if den > 0.0 { num / den } else { 0.0 }
|
||||
}
|
||||
|
||||
fn compute_torso_jitter(pred_kpts: &[Array2<f32>], visibility: &[Array1<f32>]) -> f32 {
|
||||
if pred_kpts.len() < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Compute torso centroid per frame.
|
||||
let centroids: Vec<Option<(f32, f32)>> = pred_kpts
|
||||
.iter()
|
||||
.zip(visibility.iter())
|
||||
.map(|(kp, vis)| {
|
||||
let mut cx = 0.0_f32;
|
||||
let mut cy = 0.0_f32;
|
||||
let mut count = 0_usize;
|
||||
for &idx in &TORSO_INDICES {
|
||||
if vis[idx] >= 0.5 {
|
||||
cx += kp[[idx, 0]];
|
||||
cy += kp[[idx, 1]];
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
if count > 0 {
|
||||
Some((cx / count as f32, cy / count as f32))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Frame-to-frame displacement squared.
|
||||
let mut sum_sq = 0.0_f64;
|
||||
let mut n_pairs = 0_usize;
|
||||
for i in 1..centroids.len() {
|
||||
if let (Some((x0, y0)), Some((x1, y1))) = (centroids[i - 1], centroids[i]) {
|
||||
let dx = (x1 - x0) as f64;
|
||||
let dy = (y1 - y0) as f64;
|
||||
sum_sq += dx * dx + dy * dy;
|
||||
n_pairs += 1;
|
||||
}
|
||||
}
|
||||
|
||||
if n_pairs == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
(sum_sq / n_pairs as f64).sqrt() as f32
|
||||
}
|
||||
|
||||
fn compute_p95_max_error(per_kp_errors: &[Vec<f32>]) -> f32 {
|
||||
// Collect all per-keypoint errors, find 95th percentile.
|
||||
let mut all_errors: Vec<f32> = per_kp_errors.iter().flat_map(|e| e.iter().copied()).collect();
|
||||
if all_errors.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
all_errors.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||||
let idx = ((all_errors.len() as f64 * 0.95) as usize).min(all_errors.len() - 1);
|
||||
all_errors[idx]
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ndarray::{array, Array1, Array2};
|
||||
|
||||
fn make_perfect_kpts() -> (Array2<f32>, Array2<f32>, Array1<f32>) {
|
||||
let kp = Array2::from_shape_fn((17, 2), |(j, d)| {
|
||||
if d == 0 { j as f32 * 0.05 } else { j as f32 * 0.03 }
|
||||
});
|
||||
let vis = Array1::ones(17);
|
||||
(kp.clone(), kp, vis)
|
||||
}
|
||||
|
||||
fn make_noisy_kpts(noise: f32) -> (Array2<f32>, Array2<f32>, Array1<f32>) {
|
||||
let gt = Array2::from_shape_fn((17, 2), |(j, d)| {
|
||||
if d == 0 { j as f32 * 0.05 } else { j as f32 * 0.03 }
|
||||
});
|
||||
let pred = Array2::from_shape_fn((17, 2), |(j, d)| {
|
||||
gt[[j, d]] + noise * ((j * 7 + d * 3) as f32).sin()
|
||||
});
|
||||
let vis = Array1::ones(17);
|
||||
(pred, gt, vis)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn joint_error_perfect_predictions_pass() {
|
||||
let (pred, gt, vis) = make_perfect_kpts();
|
||||
let result = evaluate_joint_error(
|
||||
&[pred],
|
||||
&[gt],
|
||||
&[vis],
|
||||
&[1.0],
|
||||
&JointErrorThresholds::default(),
|
||||
);
|
||||
assert_eq!(result.pck_all, 1.0, "perfect predictions should have PCK=1.0");
|
||||
assert!((result.oks - 1.0).abs() < 1e-3, "perfect predictions should have OKS~1.0");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn joint_error_empty_returns_fail() {
|
||||
let result = evaluate_joint_error(
|
||||
&[],
|
||||
&[],
|
||||
&[],
|
||||
&[],
|
||||
&JointErrorThresholds::default(),
|
||||
);
|
||||
assert!(!result.passes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn joint_error_noisy_predictions_lower_pck() {
|
||||
let (pred, gt, vis) = make_noisy_kpts(0.1);
|
||||
let result = evaluate_joint_error(
|
||||
&[pred],
|
||||
&[gt],
|
||||
&[vis],
|
||||
&[1.0],
|
||||
&JointErrorThresholds::default(),
|
||||
);
|
||||
assert!(result.pck_all < 1.0, "noisy predictions should have PCK < 1.0");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracking_no_id_switches_pass() {
|
||||
let frames: Vec<TrackingFrame> = (0..100)
|
||||
.map(|i| TrackingFrame {
|
||||
frame_idx: i,
|
||||
gt_ids: vec![1, 2],
|
||||
pred_ids: vec![1, 2],
|
||||
assignments: vec![(1, 1), (2, 2)],
|
||||
})
|
||||
.collect();
|
||||
let result = evaluate_tracking(&frames, 1.0, &TrackingThresholds::default());
|
||||
assert_eq!(result.id_switches, 0);
|
||||
assert!(result.passes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracking_id_switches_detected() {
|
||||
let mut frames: Vec<TrackingFrame> = (0..10)
|
||||
.map(|i| TrackingFrame {
|
||||
frame_idx: i,
|
||||
gt_ids: vec![1, 2],
|
||||
pred_ids: vec![1, 2],
|
||||
assignments: vec![(1, 1), (2, 2)],
|
||||
})
|
||||
.collect();
|
||||
// Swap assignments at frame 5.
|
||||
frames[5].assignments = vec![(2, 1), (1, 2)];
|
||||
let result = evaluate_tracking(&frames, 1.0, &TrackingThresholds::default());
|
||||
assert!(result.id_switches >= 1, "should detect ID switch at frame 5");
|
||||
assert!(!result.passes, "ID switches should cause failure");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracking_empty_returns_fail() {
|
||||
let result = evaluate_tracking(&[], 1.0, &TrackingThresholds::default());
|
||||
assert!(!result.passes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn vital_signs_accurate_breathing_passes() {
|
||||
let measurements = vec![
|
||||
VitalSignMeasurement {
|
||||
breathing_bpm: 15.0,
|
||||
gt_breathing_bpm: 14.5,
|
||||
breathing_snr_db: 10.0,
|
||||
heartbeat_bpm: None,
|
||||
gt_heartbeat_bpm: None,
|
||||
heartbeat_snr_db: None,
|
||||
},
|
||||
VitalSignMeasurement {
|
||||
breathing_bpm: 16.0,
|
||||
gt_breathing_bpm: 15.5,
|
||||
breathing_snr_db: 8.0,
|
||||
heartbeat_bpm: None,
|
||||
gt_heartbeat_bpm: None,
|
||||
heartbeat_snr_db: None,
|
||||
},
|
||||
];
|
||||
let result = evaluate_vital_signs(&measurements, &VitalSignThresholds::default());
|
||||
assert!(result.breathing_error_bpm <= 2.0);
|
||||
assert!(result.passes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn vital_signs_inaccurate_breathing_fails() {
|
||||
let measurements = vec![VitalSignMeasurement {
|
||||
breathing_bpm: 25.0,
|
||||
gt_breathing_bpm: 15.0,
|
||||
breathing_snr_db: 10.0,
|
||||
heartbeat_bpm: None,
|
||||
gt_heartbeat_bpm: None,
|
||||
heartbeat_snr_db: None,
|
||||
}];
|
||||
let result = evaluate_vital_signs(&measurements, &VitalSignThresholds::default());
|
||||
assert!(!result.passes, "10 BPM error should fail");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn vital_signs_empty_returns_fail() {
|
||||
let result = evaluate_vital_signs(&[], &VitalSignThresholds::default());
|
||||
assert!(!result.passes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tier_determination_gold() {
|
||||
let je = JointErrorResult {
|
||||
pck_all: 0.85,
|
||||
pck_torso: 0.90,
|
||||
oks: 0.65,
|
||||
jitter_rms_m: 0.01,
|
||||
max_error_p95_m: 0.10,
|
||||
passes: true,
|
||||
};
|
||||
let tr = TrackingResult {
|
||||
id_switches: 0,
|
||||
fragmentation_ratio: 0.01,
|
||||
false_tracks_per_min: 0.0,
|
||||
mota: 0.95,
|
||||
n_frames: 1000,
|
||||
passes: true,
|
||||
};
|
||||
let vs = VitalSignResult {
|
||||
breathing_error_bpm: 1.0,
|
||||
breathing_snr_db: 8.0,
|
||||
heartbeat_error_bpm: Some(3.0),
|
||||
heartbeat_snr_db: Some(4.0),
|
||||
n_measurements: 10,
|
||||
passes: true,
|
||||
};
|
||||
assert_eq!(determine_tier(&je, &tr, &vs), RuViewTier::Gold);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tier_determination_silver() {
|
||||
let je = JointErrorResult { passes: true, ..Default::default() };
|
||||
let tr = TrackingResult { passes: true, ..Default::default() };
|
||||
let vs = VitalSignResult { passes: false, ..Default::default() };
|
||||
assert_eq!(determine_tier(&je, &tr, &vs), RuViewTier::Silver);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tier_determination_bronze() {
|
||||
let je = JointErrorResult { passes: false, ..Default::default() };
|
||||
let tr = TrackingResult { passes: true, ..Default::default() };
|
||||
let vs = VitalSignResult { passes: false, ..Default::default() };
|
||||
assert_eq!(determine_tier(&je, &tr, &vs), RuViewTier::Bronze);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tier_determination_fail() {
|
||||
let je = JointErrorResult { passes: true, ..Default::default() };
|
||||
let tr = TrackingResult { passes: false, ..Default::default() };
|
||||
let vs = VitalSignResult { passes: true, ..Default::default() };
|
||||
assert_eq!(determine_tier(&je, &tr, &vs), RuViewTier::Fail);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tier_ordering() {
|
||||
assert!(RuViewTier::Gold > RuViewTier::Silver);
|
||||
assert!(RuViewTier::Silver > RuViewTier::Bronze);
|
||||
assert!(RuViewTier::Bronze > RuViewTier::Fail);
|
||||
}
|
||||
|
||||
// Implement Default for test convenience.
|
||||
impl Default for JointErrorResult {
|
||||
fn default() -> Self {
|
||||
JointErrorResult {
|
||||
pck_all: 0.0,
|
||||
pck_torso: 0.0,
|
||||
oks: 0.0,
|
||||
jitter_rms_m: 0.0,
|
||||
max_error_p95_m: 0.0,
|
||||
passes: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for TrackingResult {
|
||||
fn default() -> Self {
|
||||
TrackingResult {
|
||||
id_switches: 0,
|
||||
fragmentation_ratio: 0.0,
|
||||
false_tracks_per_min: 0.0,
|
||||
mota: 0.0,
|
||||
n_frames: 0,
|
||||
passes: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for VitalSignResult {
|
||||
fn default() -> Self {
|
||||
VitalSignResult {
|
||||
breathing_error_bpm: 0.0,
|
||||
breathing_snr_db: 0.0,
|
||||
heartbeat_error_bpm: None,
|
||||
heartbeat_snr_db: None,
|
||||
n_measurements: 0,
|
||||
passes: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user