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>
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ruv
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README.md
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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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