R12 — RF weather mapping: structural drift from passive WiFi (negative-ish result + revised plan)

Status: first experiment landed — NEGATIVE-ish, with a clear next step · 2026-05-22

The 10-year vision

Every WiFi access point in a building is, incidentally, a coherent radio source flooding the structure with energy. The walls, floors, furniture, and humans inside reflect that energy with characteristic multipath signatures. The persistent-room field model in wifi-densepose-signal/src/ruvsense/field_model.rs already captures the spatial eigenstructure of those reflections to subtract the room's baseline from occupancy detection.

The R12 vision generalises that to the temporal dimension: continuously track how the building's RF eigenstructure drifts across days, weeks, months, years. The hypothesis:

• A new piece of furniture changes the multipath profile in one specific way (additional reflector at a specific location).
• Water in a wall changes the dielectric constant of that wall, shifting reflection phase + attenuation.
• A structural settlement changes the geometric placement of reflectors by sub-cm amounts, detectable via OFDM phase coherence.
• A missing ceiling tile changes Fresnel-zone coupling between rooms.
• An HVAC failure changes air humidity → changes wave-propagation constant → changes phase at long ranges.

Pre-2026 SOTA mostly uses CSI for activity recognition. The shift to structural integrity monitoring from passive ambient RF is open territory.

First experiment (this tick)

examples/research-sota/r12_rf_weather_eigenshift.py tests the simplest possible algorithm: SVD on the per-frame CSI matrix, top-K singular values, cosine distance between spectra over time.

Setup:

• Take 1,077 CSI windows from the existing paired data.
• Split first-half (10,760 frames) = "before", last-half (10,780 frames) = "after".
• Inject a synthetic structural perturbation into the "after" half: multiply 3 subcarriers ([30, 41, 52] — top-saliency from R5) by 0.85 to simulate a new reflective surface attenuating those frequencies by ~1.4 dB.
• Top-10 singular values per half. Cosine distance between spectra.

Result

	Cosine distance from BEFORE
AFTER (no perturbation, control)	0.00035
AFTER (with 3-subcarrier perturbation)	0.00024
Signal / natural-drift ratio	0.69×

Verdict: WEAK. The synthetic structural perturbation produces a smaller spectral distance than the natural temporal drift from operator movement in the same recording. The top-10 singular-value spectrum is not sensitive enough to detect ~15% attenuation on 3 of 56 subcarriers when the room's occupant is moving.

Why this fails — and how to fix it

The top-K singular-value spectrum captures the dominant energy in the channel state. A 15% perturbation on 3 of 56 subcarriers shifts the matrix by ≤(3/56) × 15% ≈ 0.8% of total energy. That's well below the natural temporal variance from a moving operator.

Three concrete revisions for next attempts:

1. Use the FULL eigenvector basis, not just the spectrum. The cosine distance on top-K singular values is scale-aware but direction-blind. Comparing the top-K eigenvectors (singular vectors) via subspace angles ("principal angles between subspaces") would catch the structural shift even when the energy distribution stays similar.

2. Detect specific subcarriers via residual analysis. Instead of comparing whole spectra, project each window onto the empty-room subspace and look for consistent per-subcarrier residuals — these would localise the perturbation. The 3 perturbed subcarriers would show a persistent attenuation bias that natural drift wouldn't reproduce.

3. Multi-day baseline. This experiment uses a single 30-min recording. The "natural temporal drift" is dominated by operator movement, not by structural change. The real RF-weather problem has the OPPOSITE noise structure: structural changes happen over hours-to-days, occupancy noise averages out over minutes-to-hours. Averaging the eigenspectrum over a 24-hour window before comparing should knock down the operator-noise floor by 50-100×.

What still holds

The 10-year vision isn't refuted — the algorithm choice was wrong. Specifically:

• The physics is real: dielectric changes in walls cause measurable CSI shifts (well-documented in 2020-era CSI building-monitoring literature).
• The hardware is sufficient: ESP32-S3's CSI bandwidth + phase resolution is enough to detect 1° phase shifts ≈ 0.5 mm displacement at 5 GHz.
• The deployment story works: any WiFi AP in a building can be sampled passively. No physical installation cost.
• The failure mode in this experiment is the algorithm + the noise structure of single-day data, not the underlying signal.

What this DOES prove

• The simple "SVD spectrum cosine distance" approach does not work in single-day data. Anyone implementing this from scratch should start with subspace angles + multi-day averaging.
• The natural temporal drift in operator-occupied data is non-negligible at the eigenvalue level — any change-detection algorithm has to model this drift explicitly rather than treat it as zero-mean noise.

What's next on this thread

• Implement principal angles between subspaces (PABS) as the comparison metric instead of cosine on singular values. PABS catches subspace rotations that singular-value cosines miss.
• Add per-subcarrier residual analysis — project each window onto the baseline subspace, store residual norms per subcarrier per window, look for persistent biases.
• Need multi-day data at minimum. Even better: 7-day data with a deliberate structural change at day 4 (e.g. move a chair 1 m). Currently no such dataset exists in the repo.

Connection back

• R5 (band-spread saliency): the perturbation chose top-saliency subcarriers, but it still wasn't detected — suggests R5's saliency is task-specific (count-task saliency ≠ structure-detection saliency). Useful counter-data point.
• R7 (multi-link consistency): the same SVD-spectrum-distance primitive did work for adversarial-node detection in R7, because there the perturbation magnitude was much larger (entire 56-subcarrier replay/shift). Confirms the algorithm's sensitivity scales with perturbation magnitude, not subtlety.
• R8 (RSSI-only): RSSI is the trace of the CSI covariance matrix. The fact that even the full top-10 spectrum can't detect this perturbation means RSSI alone definitely can't — confirms R12 is CSI-only territory, not RSSI-feasible.

10-year vertical applications (preserved despite negative result)

The vision is right; the algorithm needs work. Verticals to chase once PABS + multi-day data exist:

• Building structural monitoring for insurance companies — early water-damage detection from RF signature shift.
• Earthquake-zone foundation drift — long-baseline tracking of sub-mm geometric shifts via OFDM phase coherence.
• HVAC efficiency audits — humidity changes air's wave-propagation constant; persistent humidity bias detectable at long range.
• Museum / archive climate stability — same physics, lower allowable drift.
• Cellar-aged-wine surveillance — preposterous-sounding 20-year vertical, but the physics is identical and the volumes (premium cellar) support the BOM.