9e7fa83210
* feat(signal): ADR-134 — CSI→CIR via ISTA + NeumannSolver warm-start
End-to-end first-class Channel Impulse Response estimation in the Rust
workspace. Bridges CSI (frequency domain) to CIR (delay domain) so
multistatic coherence gating, NLOS/LOS classification, and (at HT40+)
ToF ranging become tractable in `wifi-densepose-signal`.
Algorithm: ISTA L1 sparse recovery over a normalized DFT sub-matrix
sensing operator Φ ∈ ℂ^(K×G) with G = 3K (3× super-resolution). The
Tikhonov-regularised warm start re-uses `ruvector_solver::neumann::
NeumannSolver` — same call pattern as `fresnel.rs:280` and
`train/subcarrier.rs:225` — so no new crate dependencies.
Tiers supported: HT20 / HT40 / HE20 (Tier A-HE, C6) / HE40. The C6
HE-LTF tier is the preferred Tier A target whenever an 11ax AP is in
range; firmware substrate already shipped at v0.7.0-esp32 per ADR-110.
Measured performance (release, single CirEstimator shared across 12
links): HT20 2.72 ms / HE20 3.20 ms / HT40 13.43 ms / HE40 9.71 ms per
estimate(). HT20 12-link multistatic 17.7 ms — fits the 50 ms RuvSense
cycle; HT40 12-link 74 ms exceeds it and is flagged in ADR-134 §2.7 as
requiring Rayon parallelism or G=2K super-res reduction.
Measured Φ conditioning: κ(Φ) ≈ 1.00 identically across all tiers.
ADR-134 §2.3 was corrected — the C6 advantage is statistical SNR gain
(√(242/52) ≈ 2.16×) from more independent measurements, not improved
conditioning.
Witness: bit-deterministic SHA-256 over CirEstimator output on the
synthetic ADR-028 reference signal (100 frames, top-5 taps, 1e-6
quantization). Hash committed to expected_cir_features.sha256;
verify-cir-proof.sh wires the check into the existing witness bundle.
CI: cargo test --features cir + verify-cir-proof.sh added as separate
steps under the Rust Workspace Tests job; regressions are unambiguously
attributable.
Files:
- ADR + WITNESS-LOG-028 row 34 + CLAUDE.md module count (14 → 15)
- src/ruvsense/cir.rs (~540 LOC) + lib.rs re-exports + multistatic.rs
wire-up (reversible via `use_cir_gate=false`)
- 3 integration tests + Criterion bench + 3 deterministic fixtures
- cir_proof_runner binary + sha256 + verify-cir-proof.sh
Test rate: 395 pass / 6 ignored (P2 ISTA hyperparameter tuning; see
#[ignore] reasons) / 0 fail. cargo check clean; verify-cir-proof.sh
VERDICT: PASS.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(signal): make CIR witness cross-platform-deterministic
The first witness (Windows-generated hash 89704bfd…) failed on Linux CI
with a different hash (b36741bf…). Root cause: hashing `re`/`im` parts of
top-5 taps at 1e-6 precision is too tight against libm differences in
sin/cos/sqrt across glibc, MSVC, and Apple-clang. The previous
"top-5 sorted by magnitude" form also suffered from rank instability when
taps are near-tied — libm jitter could shuffle the ordering even when the
algorithm is unchanged.
New canonical form: full per-tap quantised-magnitude profile in natural
index order, no sort.
- 156 taps × 2 bytes (u16 le) per frame = 312 bytes/frame.
- Quantisation 1e-2 — robust to ~1e-3 float drift while still tripping
on real algorithmic changes (e.g., a 10× lambda shift moves magnitudes
by >1e-2).
- No top-K selection — eliminates the unstable magnitude-sort step.
Regenerated expected_cir_features.sha256 — new hash 120bd7b1…
If the next CI run still mismatches, the cause is structural (rustfft SIMD
code path selection or NeumannSolver internal ordering), not magnitudes,
and the witness needs further coarsening or to be made platform-tagged.
Co-Authored-By: claude-flow <ruv@ruv.net>
|
||
|---|---|---|
| .. | ||
| .issue-177-body.md | ||
| ADR-001-wifi-mat-disaster-detection.md | ||
| ADR-002-ruvector-rvf-integration-strategy.md | ||
| ADR-003-rvf-cognitive-containers-csi.md | ||
| ADR-004-hnsw-vector-search-fingerprinting.md | ||
| ADR-005-sona-self-learning-pose-estimation.md | ||
| ADR-006-gnn-enhanced-csi-pattern-recognition.md | ||
| ADR-007-post-quantum-cryptography-secure-sensing.md | ||
| ADR-008-distributed-consensus-multi-ap.md | ||
| ADR-009-rvf-wasm-runtime-edge-deployment.md | ||
| ADR-010-witness-chains-audit-trail-integrity.md | ||
| ADR-011-python-proof-of-reality-mock-elimination.md | ||
| ADR-012-esp32-csi-sensor-mesh.md | ||
| ADR-013-feature-level-sensing-commodity-gear.md | ||
| ADR-014-sota-signal-processing.md | ||
| ADR-015-public-dataset-training-strategy.md | ||
| ADR-016-ruvector-integration.md | ||
| ADR-017-ruvector-signal-mat-integration.md | ||
| ADR-018-esp32-dev-implementation.md | ||
| ADR-019-sensing-only-ui-mode.md | ||
| ADR-020-rust-ruvector-ai-model-migration.md | ||
| ADR-021-vital-sign-detection-rvdna-pipeline.md | ||
| ADR-022-windows-wifi-enhanced-fidelity-ruvector.md | ||
| ADR-023-trained-densepose-model-ruvector-pipeline.md | ||
| ADR-024-contrastive-csi-embedding-model.md | ||
| ADR-025-macos-corewlan-wifi-sensing.md | ||
| ADR-026-survivor-track-lifecycle.md | ||
| ADR-027-cross-environment-domain-generalization.md | ||
| ADR-028-esp32-capability-audit.md | ||
| ADR-029-ruvsense-multistatic-sensing-mode.md | ||
| ADR-030-ruvsense-persistent-field-model.md | ||
| ADR-031-ruview-sensing-first-rf-mode.md | ||
| ADR-032-multistatic-mesh-security-hardening.md | ||
| ADR-033-crv-signal-line-sensing-integration.md | ||
| ADR-034-expo-mobile-app.md | ||
| ADR-035-live-sensing-ui-accuracy.md | ||
| ADR-036-rvf-training-pipeline-ui.md | ||
| ADR-037-multi-person-pose-detection.md | ||
| ADR-038-sublinear-goal-oriented-action-planning.md | ||
| ADR-039-esp32-edge-intelligence.md | ||
| ADR-040-wasm-programmable-sensing.md | ||
| ADR-041-wasm-module-collection.md | ||
| ADR-042-coherent-human-channel-imaging.md | ||
| ADR-043-sensing-server-ui-api-completion.md | ||
| ADR-044-geospatial-satellite-integration.md | ||
| ADR-045-amoled-display-support.md | ||
| ADR-046-android-tv-box-armbian-deployment.md | ||
| ADR-047-psychohistory-observatory-visualization.md | ||
| ADR-048-adaptive-csi-classifier.md | ||
| ADR-049-cross-platform-wifi-interface-detection.md | ||
| ADR-050-provisioning-tool-enhancements.md | ||
| ADR-050-quality-engineering-security-hardening.md | ||
| ADR-052-ddd-bounded-contexts.md | ||
| ADR-052-tauri-desktop-frontend.md | ||
| ADR-053-ui-design-system.md | ||
| ADR-054-desktop-full-implementation.md | ||
| ADR-055-integrated-sensing-server.md | ||
| ADR-056-ruview-desktop-capabilities.md | ||
| ADR-057-firmware-csi-build-guard.md | ||
| ADR-058-ruvector-wasm-browser-pose-example.md | ||
| ADR-059-live-esp32-csi-pipeline.md | ||
| ADR-060-provision-channel-mac-filter.md | ||
| ADR-061-qemu-esp32s3-firmware-testing.md | ||
| ADR-062-qemu-swarm-configurator.md | ||
| ADR-063-mmwave-sensor-fusion.md | ||
| ADR-064-multimodal-ambient-intelligence.md | ||
| ADR-065-happiness-scoring-seed-bridge.md | ||
| ADR-066-esp32-swarm-seed-coordinator.md | ||
| ADR-067-ruvector-v2.0.5-upgrade.md | ||
| ADR-068-per-node-state-pipeline.md | ||
| ADR-069-cognitum-seed-csi-pipeline.md | ||
| ADR-070-self-supervised-pretraining.md | ||
| ADR-071-ruvllm-training-pipeline.md | ||
| ADR-072-wiflow-architecture.md | ||
| ADR-073-multifrequency-mesh-scan.md | ||
| ADR-074-spiking-neural-csi-sensing.md | ||
| ADR-075-mincut-person-separation.md | ||
| ADR-076-csi-spectrogram-embeddings.md | ||
| ADR-077-novel-rf-sensing-applications.md | ||
| ADR-078-multifreq-mesh-applications.md | ||
| ADR-079-camera-ground-truth-training.md | ||
| ADR-080-qe-remediation-plan.md | ||
| ADR-081-adaptive-csi-mesh-firmware-kernel.md | ||
| ADR-082-pose-tracker-confirmed-output-filter.md | ||
| ADR-083-per-cluster-pi-compute-hop.md | ||
| ADR-084-rabitq-similarity-sensor.md | ||
| ADR-085-rabitq-pipeline-expansion.md | ||
| ADR-086-edge-novelty-gate.md | ||
| ADR-089-nvsim-nv-diamond-simulator.md | ||
| ADR-090-nvsim-lindblad-extension.md | ||
| ADR-091-stand-off-radar-tier-research.md | ||
| ADR-092-nvsim-dashboard-implementation.md | ||
| ADR-093-dashboard-gap-analysis.md | ||
| ADR-094-pointcloud-github-pages-deployment.md | ||
| ADR-095-rvcsi-edge-rf-sensing-platform.md | ||
| ADR-096-rvcsi-ffi-crate-layout.md | ||
| ADR-097-adopt-rvcsi-as-ruview-csi-runtime.md | ||
| ADR-098-evaluate-midstream-fit.md | ||
| ADR-099-midstream-introspection-tap.md | ||
| ADR-100-cog-packaging-specification.md | ||
| ADR-101-pose-estimation-cog.md | ||
| ADR-102-edge-module-registry.md | ||
| ADR-103-learned-multi-person-counter.md | ||
| ADR-104-ruview-mcp-cli-distribution.md | ||
| ADR-105-federated-csi-training.md | ||
| ADR-106-dp-sgd-and-primitive-isolation.md | ||
| ADR-107-cross-installation-federation.md | ||
| ADR-108-kyber-post-quantum-key-exchange.md | ||
| ADR-109-dilithium-pqc-signatures.md | ||
| ADR-110-esp32-c6-firmware-extension.md | ||
| ADR-113-multistatic-placement-strategy.md | ||
| ADR-114-cog-quantum-vitals.md | ||
| ADR-115-home-assistant-integration.md | ||
| ADR-116-cog-ha-matter-seed.md | ||
| ADR-117-pip-wifi-densepose-modernization.md | ||
| ADR-118-bfld-beamforming-feedback-layer-for-detection.md | ||
| ADR-119-bfld-frame-format-and-wire-protocol.md | ||
| ADR-120-bfld-privacy-class-and-hash-rotation.md | ||
| ADR-121-bfld-identity-risk-scoring.md | ||
| ADR-122-bfld-ruview-ha-matter-exposure.md | ||
| ADR-123-bfld-capture-path-nexmon-and-esp32.md | ||
| ADR-124-rvagent-mcp-ruvector-npm-integration.md | ||
| ADR-125-ruview-apple-home-native-hap-bridge.md | ||
| ADR-126-ruview-native-ha-port-master.md | ||
| ADR-127-homecore-state-machine-rust.md | ||
| ADR-128-homecore-integration-plugin-system.md | ||
| ADR-129-homecore-automation-engine.md | ||
| ADR-130-homecore-rest-websocket-api.md | ||
| ADR-133-homecore-assist-ruflo.md | ||
| ADR-134-csi-to-cir-time-domain-multipath.md | ||
| README.md | ||
README.md
Architecture Decision Records
This folder contains 45 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.
Why ADRs?
Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.
ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:
-
Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.
-
AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.
-
Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.
ADRs and Domain-Driven Design
The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:
- ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
- DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
- Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.
How ADRs are structured
Each ADR follows a consistent format:
- Context — What problem or gap prompted this decision
- Decision — What we chose to do and how
- Consequences — What improved, what got harder, and what risks remain
- References — Related ADRs, papers, and code paths
Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).
ADR Index
Hardware and firmware
| ADR | Title | Status |
|---|---|---|
| ADR-012 | ESP32 CSI Sensor Mesh for Distributed Sensing | Accepted (partial) |
| ADR-018 | ESP32 Development Implementation Path | Proposed |
| ADR-028 | ESP32 Capability Audit and Witness Record | Accepted |
| ADR-029 | RuvSense Multistatic Sensing Mode (TDM, channel hopping) | Proposed |
| ADR-032 | Multistatic Mesh Security Hardening | Accepted |
| ADR-039 | ESP32-S3 Edge Intelligence Pipeline (on-device vitals) | Accepted (hardware-validated) |
| ADR-040 | WASM Programmable Sensing (Tier 3) | Accepted |
| ADR-041 | WASM Module Collection (65 edge modules) | Accepted (hardware-validated) |
| ADR-044 | Provisioning Tool Enhancements | Proposed |
| ADR-110 | ESP32-C6 firmware extension — Wi-Fi 6 / 802.15.4 / TWT / LP-core | Accepted, P1-P10 complete, firmware-side substrate closed at v0.7.0-esp32. Companion docs: WITNESS-LOG-110 (13 §A0.x entries · 99.56 % cross-board RX · 104.1 µs smoothed sync stdev · ≤100 µs target met), ADR-110-REVIEW-GUIDE (one-page reviewer tour), ADR-110-BRANCH-STATE (coordination map vs feat/adr-115-ha-mqtt-matter). Host decoders + tests: Python SyncPacketParser (10) + Rust wifi_densepose_hardware::SyncPacket (15), cross-language hex pin gates drift. |
Signal processing and sensing
| ADR | Title | Status |
|---|---|---|
| ADR-013 | Feature-Level Sensing on Commodity Gear | Accepted |
| ADR-014 | SOTA Signal Processing Algorithms | Accepted |
| ADR-021 | Vital Sign Detection (breathing, heart rate) | Partial |
| ADR-030 | Persistent Field Model and Drift Detection | Proposed |
| ADR-033 | CRV Signal Line Sensing Integration | Proposed |
| ADR-037 | Multi-Person Pose Detection from Single ESP32 | Proposed |
| ADR-042 | Coherent Human Channel Imaging (beyond CSI) | Proposed |
| ADR-134 | First-Class Channel Impulse Response (CIR) Support | Proposed |
Machine learning and training
| ADR | Title | Status |
|---|---|---|
| ADR-005 | SONA Self-Learning for Pose Estimation | Partial |
| ADR-006 | GNN-Enhanced CSI Pattern Recognition | Partial |
| ADR-015 | Public Dataset Strategy (MM-Fi, Wi-Pose) | Accepted |
| ADR-016 | RuVector Training Pipeline Integration | Accepted |
| ADR-017 | RuVector Signal + MAT Integration | Proposed |
| ADR-020 | Migrate AI Inference to Rust (ONNX Runtime) | Accepted |
| ADR-023 | Trained DensePose Model with RuVector Pipeline | Proposed |
| ADR-024 | Project AETHER: Contrastive CSI Embeddings | Required |
| ADR-027 | Project MERIDIAN: Cross-Environment Generalization | Proposed |
Platform and UI
| ADR | Title | Status |
|---|---|---|
| ADR-019 | Sensing-Only UI with Gaussian Splats | Accepted |
| ADR-022 | Windows WiFi Enhanced Fidelity (multi-BSSID) | Partial |
| ADR-025 | macOS CoreWLAN WiFi Sensing | Proposed |
| ADR-031 | RuView Sensing-First RF Mode | Proposed |
| ADR-034 | Expo React Native Mobile App | Accepted |
| ADR-035 | Live Sensing UI Accuracy and Data Transparency | Accepted |
| ADR-036 | Training Pipeline UI Integration | Proposed |
| ADR-043 | Sensing Server UI API Completion (14 endpoints) | Accepted |
| ADR-115 | Home Assistant integration via MQTT auto-discovery + Matter bridge (HA-DISCO + HA-FABRIC + HA-MIND) | Accepted (MQTT track) / Proposed (Matter SDK P8b) |
Architecture and infrastructure
| ADR | Title | Status |
|---|---|---|
| ADR-001 | WiFi-Mat Disaster Detection Architecture | Accepted |
| ADR-002 | RuVector RVF Integration Strategy | Superseded |
| ADR-003 | RVF Cognitive Containers for CSI | Proposed |
| ADR-004 | HNSW Vector Search for Fingerprinting | Partial |
| ADR-007 | Post-Quantum Cryptography for Sensing | Proposed |
| ADR-008 | Distributed Consensus for Multi-AP | Proposed |
| ADR-009 | RVF WASM Runtime for Edge Deployment | Proposed |
| ADR-010 | Witness Chains for Audit Trail Integrity | Proposed |
| ADR-011 | Proof-of-Reality and Mock Elimination | Proposed |
| ADR-026 | Survivor Track Lifecycle (MAT crate) | Accepted |
| ADR-038 | Sublinear GOAP for Roadmap Optimization | Proposed |
| ADR-095 | rvCSI — Edge RF Sensing Runtime Platform | Proposed |
| ADR-096 | rvCSI — Crate Topology, the napi-c Shim, and the napi-rs Node Surface | Proposed |
| ADR-097 | Adopt rvCSI as RuView's primary CSI runtime (phased adoption) | Proposed |
| ADR-098 | Evaluate ruvnet/midstream for RuView's CSI / WebSocket / mesh pipeline |
Rejected |
| ADR-099 | Adopt midstream as RuView's real-time introspection + low-latency tap | Proposed |
Related
- DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
- User Guide — Setup, API reference, and hardware instructions
- Build Guide — Building from source