The deep end: multistatic mesh, tomography, persistent field models, and the security model that protects them. Most of this lives in wifi-densepose-signal/src/ruvsense/ (14 modules) and wifi-densepose-ruvector/src/viewpoint/ (5 modules).
Treat every WiFi link in range — including neighbours' APs — as a bistatic radar pair, then fuse them.
Module (signal/src/ruvsense/) |
Purpose |
|---|---|
multiband.rs |
Multi-band CSI frame fusion, cross-channel coherence |
phase_align.rs |
Iterative LO phase-offset estimation, circular mean |
multistatic.rs |
Attention-weighted fusion, geometric diversity |
coherence.rs / coherence_gate.rs |
Z-score coherence scoring; Accept / PredictOnly / Reject / Recalibrate gate decisions |
pose_tracker.rs |
17-keypoint Kalman tracker with AETHER re-ID embeddings |
field_model.rs |
SVD room eigenstructure, perturbation extraction |
tomography.rs |
RF tomography, ISTA L1 solver, voxel grid |
longitudinal.rs |
Welford stats, biomechanics drift detection |
intention.rs |
Pre-movement lead signals (200–500 ms ahead) |
cross_room.rs |
Environment fingerprinting, transition graph |
gesture.rs |
DTW template-matching gesture classifier |
adversarial.rs |
Physically-impossible-signal detection, multi-link consistency |
Combine 2+ nodes geometrically — more nodes, more independent looks, tighter localization.
Module (ruvector/src/viewpoint/) |
Purpose |
|---|---|
attention.rs |
CrossViewpointAttention, GeometricBias, softmax with G_bias |
geometry.rs |
GeometricDiversityIndex, Cramér–Rao bounds, Fisher Information |
coherence.rs |
Phase-phasor coherence, hysteresis gate |
fusion.rs |
MultistaticArray aggregate root, domain events |
Host-side helpers to explore the geometry before deploying: node scripts/mesh-graph-transformer.js, node scripts/passive-radar.js, node scripts/deep-scan.js.
field_model.rs builds an SVD eigenstructure of the room and stores it (RVF, ideally on a Cognitum Seed). New CSI frames are projected against it; the residual is the perturbation. Lets you ask "what's different from the empty-room baseline?" and survive restarts.
tomography.rs reconstructs a voxel occupancy grid from the multistatic link set via an ISTA L1 solver (sparse — most voxels are empty). Use with cross-viewpoint geometry for through-wall volumetric imaging. RuVector solver crates back the sparse interpolation (114→56 subcarriers).
Using neighbours' APs as illuminators and pooling links across a mesh expands the attack surface. Mitigations:
adversarial.rs rejects physically impossible signals and cross-checks multi-link consistency.coherence_gate.rs quarantines low-coherence / suspicious links (Reject / Recalibrate).ruview-verify and docs/security-audit-wasm-edge-vendor.md).cd v2 && cargo test --workspace --no-default-features # incl. ruvsense + viewpoint tests
cargo test -p wifi-densepose-signal --no-default-features
cargo test -p wifi-densepose-ruvector --no-default-features
cd .. && python archive/v1/data/proof/verify.py
v2/crates/wifi-densepose-signal/src/ruvsense/ · v2/crates/wifi-densepose-ruvector/src/viewpoint/
docs/research/, docs/security-audit-wasm-edge-vendor.md