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docs: update user guide with v0.3.0 features — multistatic mesh, CRV, QUIC, crates.io

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- Test count 700+ → 1,100+, ADR count 27 → 33, Rust version 1.75+
- Add crates.io installation section (cargo add for all 15 crates)
- Add ESP32 multistatic mesh section (TDM, channel hopping, QUIC transport)
- Add mesh key provisioning and TDM slot assignment instructions
- Add CRV signal-line protocol section with 6-stage table
- Update vital signs range for multistatic mesh (~8 m)
- Update through-wall FAQ with multistatic mesh capabilities
- Update ESP32 hardware setup with secure provisioning and ADR refs

Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
2026-03-02 08:46:09 -05:00
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docs/user-guide.md
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@@ -10,6 +10,7 @@ WiFi DensePose turns commodity WiFi signals into real-time human pose estimation
2. [Installation](#installation)
- [Docker (Recommended)](#docker-recommended)
- [From Source (Rust)](#from-source-rust)
- [From crates.io](#from-cratesio-individual-crates)
- [From Source (Python)](#from-source-python)
- [Guided Installer](#guided-installer)
3. [Quick Start](#quick-start)
@@ -19,12 +20,14 @@ WiFi DensePose turns commodity WiFi signals into real-time human pose estimation
- [Simulated Mode (No Hardware)](#simulated-mode-no-hardware)
- [Windows WiFi (RSSI Only)](#windows-wifi-rssi-only)
- [ESP32-S3 (Full CSI)](#esp32-s3-full-csi)
- [ESP32 Multistatic Mesh (Advanced)](#esp32-multistatic-mesh-advanced)
5. [REST API Reference](#rest-api-reference)
6. [WebSocket Streaming](#websocket-streaming)
7. [Web UI](#web-ui)
8. [Vital Sign Detection](#vital-sign-detection)
9. [CLI Reference](#cli-reference)
10. [Training a Model](#training-a-model)
- [CRV Signal-Line Protocol](#crv-signal-line-protocol)
11. [RVF Model Containers](#rvf-model-containers)
12. [Hardware Setup](#hardware-setup)
- [ESP32-S3 Mesh](#esp32-s3-mesh)
@@ -79,12 +82,41 @@ cd wifi-densepose/rust-port/wifi-densepose-rs
# Build
cargo build --release
# Verify (runs 700+ tests)
# Verify (runs 1,100+ tests)
cargo test --workspace
```
The compiled binary is at `target/release/sensing-server`.
### From crates.io (Individual Crates)
All 15 crates are published to crates.io at v0.3.0. Add individual crates to your own Rust project:
```bash
# Core types and traits
cargo add wifi-densepose-core
# Signal processing (includes RuvSense multistatic sensing)
cargo add wifi-densepose-signal
# Neural network inference
cargo add wifi-densepose-nn
# Mass Casualty Assessment Tool
cargo add wifi-densepose-mat
# ESP32 hardware + TDM protocol + QUIC transport
cargo add wifi-densepose-hardware
# RuVector integration (add --features crv for CRV signal-line protocol)
cargo add wifi-densepose-ruvector --features crv
# WebAssembly bindings
cargo add wifi-densepose-wasm
```
See the full crate list and dependency order in [CLAUDE.md](../CLAUDE.md#crate-publishing-order).
### From Source (Python)
```bash
@@ -231,6 +263,27 @@ docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:late
The ESP32 nodes stream binary CSI frames over UDP to port 5005. See [Hardware Setup](#esp32-s3-mesh) for flashing instructions.
### ESP32 Multistatic Mesh (Advanced)
For higher accuracy with through-wall tracking, deploy 3-6 ESP32-S3 nodes in a **multistatic mesh** configuration. Each node acts as both transmitter and receiver, creating multiple sensing paths through the environment.
```bash
# Start the aggregator with multistatic mode
./target/release/sensing-server --source esp32 --udp-port 5005 --http-port 3000 --ws-port 3001
```
The mesh uses a **Time-Division Multiplexing (TDM)** protocol so nodes take turns transmitting, avoiding self-interference. Key features:
| Feature | Description |
|---------|-------------|
| TDM coordination | Nodes cycle through TX/RX slots (configurable guard intervals) |
| Channel hopping | Automatic 2.4/5 GHz band cycling for multiband fusion |
| QUIC transport | TLS 1.3-encrypted streams on aggregator nodes (ADR-032a) |
| Manual crypto fallback | HMAC-SHA256 beacon auth on constrained ESP32-S3 nodes |
| Attention-weighted fusion | Cross-viewpoint attention with geometric diversity bias |
See [ADR-029](adr/ADR-029-ruvsense-multistatic-sensing-mode.md) and [ADR-032](adr/ADR-032-multistatic-mesh-security-hardening.md) for the full design.
---
## REST API Reference
@@ -369,7 +422,7 @@ The system extracts breathing rate and heart rate from CSI signal fluctuations u
**Requirements:**
- CSI-capable hardware (ESP32-S3 or research NIC) for accurate readings
- Subject within ~3-5 meters of an access point
- Subject within ~3-5 meters of an access point (up to ~8 m with multistatic mesh)
- Relatively stationary subject (large movements mask vital sign oscillations)
**Simulated mode** produces synthetic vital sign data for testing.
@@ -493,6 +546,26 @@ MERIDIAN components (all pure Rust, +12K parameters):
See [ADR-027](adr/ADR-027-cross-environment-domain-generalization.md) for the full design.
### CRV Signal-Line Protocol
The CRV (Coordinate Remote Viewing) signal-line protocol (ADR-033) maps a 6-stage cognitive sensing methodology onto WiFi CSI processing. This enables structured anomaly classification and multi-person disambiguation.
| Stage | CRV Term | WiFi Mapping |
|-------|----------|-------------|
| I | Gestalt | Detrended autocorrelation → periodicity / chaos / transient classification |
| II | Sensory | 6-modality CSI feature encoding (texture, temperature, luminosity, etc.) |
| III | Topology | AP mesh topology graph with link quality weights |
| IV | Coherence | Phase phasor coherence gate (Accept/PredictOnly/Reject/Recalibrate) |
| V | Interrogation | Person-specific signal extraction with targeted subcarrier selection |
| VI | Partition | Multi-person partition with cross-room convergence scoring |
```bash
# Enable CRV in your Cargo.toml
cargo add wifi-densepose-ruvector --features crv
```
See [ADR-033](adr/ADR-033-crv-signal-line-sensing-integration.md) for the full design.
---
## RVF Model Containers
@@ -535,7 +608,7 @@ A 3-6 node ESP32-S3 mesh provides full CSI at 20 Hz. Total cost: ~$54 for a 3-no
**What you need:**
- 3-6x ESP32-S3 development boards (~$8 each)
- A WiFi router (the CSI source)
- A computer running the sensing server
- A computer running the sensing server (aggregator)
**Flashing firmware:**
@@ -557,6 +630,33 @@ python scripts/provision.py --port COM7 \
Replace `192.168.1.20` with the IP of the machine running the sensing server.
**Mesh key provisioning (secure mode):**
For multistatic mesh deployments with authenticated beacons (ADR-032), provision a shared mesh key:
```bash
python scripts/provision.py --port COM7 \
--ssid "YourWiFi" --password "YourPassword" --target-ip 192.168.1.20 \
--mesh-key "$(openssl rand -hex 32)"
```
All nodes in a mesh must share the same 256-bit mesh key for HMAC-SHA256 beacon authentication. The key is stored in ESP32 NVS flash and zeroed on firmware erase.
**TDM slot assignment:**
Each node in a multistatic mesh needs a unique TDM slot ID (0-based):
```bash
# Node 0 (slot 0) — first transmitter
python scripts/provision.py --port COM7 --tdm-slot 0 --tdm-total 3
# Node 1 (slot 1)
python scripts/provision.py --port COM8 --tdm-slot 1 --tdm-total 3
# Node 2 (slot 2)
python scripts/provision.py --port COM9 --tdm-slot 2 --tdm-total 3
```
**Start the aggregator:**
```bash
@@ -567,7 +667,7 @@ Replace `192.168.1.20` with the IP of the machine running the sensing server.
docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest --source esp32
```
See [ADR-018](../docs/adr/ADR-018-esp32-dev-implementation.md) and [Tutorial #34](https://github.com/ruvnet/wifi-densepose/issues/34).
See [ADR-018](../docs/adr/ADR-018-esp32-dev-implementation.md), [ADR-029](../docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md), and [Tutorial #34](https://github.com/ruvnet/wifi-densepose/issues/34).
### Intel 5300 / Atheros NIC
@@ -626,7 +726,7 @@ docker run -p 3000:3000 -p 3001:3001 ruvnet/wifi-densepose:latest
### Build: Rust compilation errors
Ensure Rust 1.70+ is installed:
Ensure Rust 1.75+ is installed (1.85+ recommended):
```bash
rustup update stable
rustc --version
@@ -656,7 +756,7 @@ No. Consumer WiFi exposes only RSSI (one number per access point), not CSI (56+
Accuracy depends on hardware and environment. With a 3-node ESP32 mesh in a single room, the system tracks 17 COCO keypoints. The core algorithm follows the CMU "DensePose From WiFi" paper ([arXiv:2301.00250](https://arxiv.org/abs/2301.00250)). The MERIDIAN domain generalization system (ADR-027) reduces cross-environment accuracy loss from 40-70% to under 15% via 10-second automatic calibration.
**Q: Does it work through walls?**
Yes. WiFi signals penetrate non-metallic materials (drywall, wood, concrete up to ~30cm). Metal walls/doors significantly attenuate the signal. The effective through-wall range is approximately 5 meters.
Yes. WiFi signals penetrate non-metallic materials (drywall, wood, concrete up to ~30cm). Metal walls/doors significantly attenuate the signal. With a single AP the effective through-wall range is approximately 5 meters. With a 3-6 node multistatic mesh (ADR-029), attention-weighted cross-viewpoint fusion extends the effective range to ~8 meters through standard residential walls.
**Q: How many people can it track?**
Each access point can distinguish ~3-5 people with 56 subcarriers. Multi-AP deployments multiply linearly (e.g., 4 APs cover ~15-20 people). There is no hard software limit; the practical ceiling is signal physics.
@@ -671,7 +771,7 @@ The Rust implementation (v2) is 810x faster than Python (v1) for the full CSI pi
## Further Reading
- [Architecture Decision Records](../docs/adr/) - 27 ADRs covering all design decisions
- [Architecture Decision Records](../docs/adr/) - 33 ADRs covering all design decisions
- [WiFi-Mat Disaster Response Guide](wifi-mat-user-guide.md) - Search & rescue module
- [Build Guide](build-guide.md) - Detailed build instructions
- [RuVector](https://github.com/ruvnet/ruvector) - Signal intelligence crate ecosystem