wifi-densepose/examples/research-sota
rUv a1bbe2e8a6
research(R1): ToA CRLB — precision floor for WiFi multistatic localisation (#711)
Quantitative Cramer-Rao Lower Bound analysis for WiFi ranging via both
Time-of-Arrival and phase-based methods, with multistatic 4-anchor
position-error budget.

Headline (20 MHz HT20, 20 dB SNR, 100 averaged frames):
- ToA range CRLB:     4.1 cm
- Phase (5 deg noise): 0.17 mm
- Phase advantage:    240x (after ambiguity resolution)

4-anchor convex-hull room (GDOP 1.5):
- ToA position precision:   25 cm  (room-pose-quality floor)
- Phase position precision:  1 mm  (RTK-quality, ambiguity-resolved)

This is the strongest architectural lever this loop has surfaced for
ADR-029 (multistatic sensing). The current learning-based attention
approach has no provable precision floor; an explicit ToA-then-phase
pipeline sits within 2x of CRLB by Kay's theory.

Composes cleanly with R6:
- R6 gives the spatial sensitivity envelope (40 cm Fresnel at 2.4 GHz)
- R1 gives the ranging precision within it (1 mm phase, 4 cm ToA averaged)
- Independent, additive, together bound full multistatic geometry budget

Closes a gap R10 created: foliage drops SNR, which directly worsens
ToA CRLB. A 50 m foliage link at 5 dB SNR drops to ~1 m ToA precision.
R10's 100 m sparse-foliage range is *detectable* not *localisable*.

Honest scope:
- CRLB is a lower bound; real estimators sit 1-2x above it
- 5 deg phase noise assumes phase_align.rs is applied
- Multipath degrades CRLB by 2-5x even with MUSIC super-resolution
- Integer-ambiguity (cycle-slip) is unsolved per-subcarrier; needs
  multi-subcarrier wide-lane unwrap

Coordination: ticks/tick-9.md, no PROGRESS.md edit.
2026-05-22 01:38:35 -04:00
..
r1_toa_crlb.py research(R1): ToA CRLB — precision floor for WiFi multistatic localisation (#711) 2026-05-22 01:38:35 -04:00
r1_toa_crlb_results.json research(R1): ToA CRLB — precision floor for WiFi multistatic localisation (#711) 2026-05-22 01:38:35 -04:00
r5_subcarrier_saliency.py research(sota): kick off SOTA research loop + first R5 saliency measurement (#702) 2026-05-21 23:05:55 -04:00
r6_fresnel_results.json research(R6): Fresnel-zone forward model — bedrock physics for CSI sensitivity (#710) 2026-05-22 01:31:09 -04:00
r6_fresnel_zone.py research(R6): Fresnel-zone forward model — bedrock physics for CSI sensitivity (#710) 2026-05-22 01:31:09 -04:00
r7_multilink_consistency.py research(R7): Stoer-Wagner mincut detects adversarial CSI nodes 3/3 in synthetic (#704) 2026-05-21 23:28:46 -04:00
r7_multilink_consistency_results.json research(R7): Stoer-Wagner mincut detects adversarial CSI nodes 3/3 in synthetic (#704) 2026-05-21 23:28:46 -04:00
r8_rssi_only_count.py research(R8): RSSI-only person count retains 95% of full-CSI accuracy (#703) 2026-05-21 23:18:09 -04:00
r8_rssi_only_results.json research(R8): RSSI-only person count retains 95% of full-CSI accuracy (#703) 2026-05-21 23:18:09 -04:00
r9_rssi_fingerprint_knn.py feat(tools/ruview-mcp): M2 — wire real inference via cog health (#706) 2026-05-21 23:43:32 -04:00
r9_rssi_fingerprint_results.json feat(tools/ruview-mcp): M2 — wire real inference via cog health (#706) 2026-05-21 23:43:32 -04:00
r10_foliage_attenuation.py research(R10): through-foliage wildlife sensing — physics feasibility + per-species gait taxonomy 2026-05-22 00:59:11 -04:00
r10_foliage_results.json research(R10): through-foliage wildlife sensing — physics feasibility + per-species gait taxonomy 2026-05-22 00:59:11 -04:00
r12_rf_weather_eigenshift.py research(R12): RF weather mapping eigenshift — negative-ish, with clearly-actionable revision path (#707) 2026-05-21 23:52:49 -04:00
r12_rf_weather_results.json research(R12): RF weather mapping eigenshift — negative-ish, with clearly-actionable revision path (#707) 2026-05-21 23:52:49 -04:00