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wifi-densepose/plugins/ruview/commands/ruview-advanced.md

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description argument-hint
Use advanced RuView capabilities — multistatic sensing, cross-viewpoint fusion, RF tomography, persistent field model, intention signals, adversarial detection, mesh security. [multistatic|cross-viewpoint|tomography|field-model|intention|adversarial|security]

/ruview-advanced

Drive RuView's research-grade / multi-node features.

  1. Invoke the ruview-advanced-sensing skill.
  2. Route on $ARGUMENTS:
    • multistatic (ADR-029) — wifi-densepose-signal/src/ruvsense/multistatic.rs, phase_align.rs, coherence_gate.rs; neighbours' APs as illuminators.
    • cross-viewpoint (ADR-016 viewpoint) — wifi-densepose-ruvector/src/viewpoint/; needs 2+ nodes; node scripts/mesh-graph-transformer.js.
    • tomography — ruvsense/tomography.rs (ISTA L1 voxel solver) + cross-viewpoint geometry; through-wall volumetric.
    • field-model (ADR-030) — ruvsense/field_model.rs, SVD room eigenstructure persisted to RVF (Cognitum Seed); residual = perturbation.
    • intention — ruvsense/intention.rs, 200–500 ms pre-movement lead signals.
    • adversarial — ruvsense/adversarial.rs, physically-impossible-signal + multi-link consistency checks.
    • security (ADR-032) — mesh hardening: adversarial gate + coherence quarantine + Ed25519 witness chain; run a security review (docs/security-audit-wasm-edge-vendor.md), see /ruview-verify.
  3. Validate: cd v2 && cargo test -p wifi-densepose-signal --no-default-features && cargo test -p wifi-densepose-ruvector --no-default-features, then python archive/v1/data/proof/verify.py.