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Enables advanced RuView multistatic sensing, RF tomography, cross-viewpoint fusion, and adversarial signal detection for research-grade or multi-node WiFi sensing deployments.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ruview:ruview-advanced-sensingThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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).
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.mdnpx claudepluginhub atiqrehman74/eagle-eye --plugin ruviewEnables advanced RuView multistatic sensing, RF tomography, cross-viewpoint fusion, and adversarial signal detection for research-grade or multi-node WiFi sensing deployments.
Wi-Fi reconnaissance methodology covering adapter selection, monitor mode and injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, client probe analysis, and OUI lookup for authorized security assessments.
Conducts passive wireless security assessments with Kismet to detect rogue access points, hidden SSIDs, weak encryption, and unauthorized clients via RF monitoring.