research(R12): RF weather mapping eigenshift — negative-ish, with clearly-actionable revision path (#707)

Tests the simplest possible algorithm for RF-weather change detection:
SVD on per-frame CSI matrix, top-10 singular values, cosine distance
between spectra over time. Hypothesis: a synthetic structural
perturbation (15 percent attenuation on 3 top-saliency subcarriers)
should produce a larger spectral shift than natural temporal drift
from operator movement in the same recording.

Result honestly: it does not. The perturbation distance (0.00024) is
*smaller* than the control distance (0.00035) — signal/drift ratio
0.69x. The top-K SVD-spectrum cosine is too coarse to detect
small-magnitude subcarrier-specific structural changes against an
operator-noise background.

Three concrete fixes identified for follow-up ticks:
1. Principal angles between subspaces (PABS), not cosine on singular
   values — catches subspace rotations the spectrum misses
2. Per-subcarrier residual analysis after projecting onto baseline
   subspace — localises the perturbation
3. Multi-day baseline — knocks down operator-noise floor by 50-100x

Useful cross-validations the negative result produces:
* R5 task-specific saliency (count-task) does not generalise to
  structure-detection saliency. Same data, different relevant
  features. Publishable distinction.
* R12 is CSI-only territory — RSSI is the trace of the CSI
  covariance, so if top-10 SVD-spectrum can't see this, RSSI can't
  either. Bounds R8 commercial-enablement story to counting only.
* R7 SVD-spectrum primitive that worked for adversarial detection
  fails here at lower perturbation magnitude. Sensitivity does NOT
  scale with subtlety — confirms the algorithm is magnitude-dominated.

Long-horizon vision (building structural monitoring, earthquake drift,
HVAC audits, climate-controlled-archive surveillance) preserved in the
research note — the physics is right, the hardware is sufficient,
the deployment story works. Just need PABS + multi-day data.

Coordination note: this tick avoided PROGRESS.md edits entirely
because horizon-tracker is concurrently editing it. Tick-5 summary
written to ticks/tick-5.md (new self-contained convention) so the
08:00 ET final summary can consolidate without conflicts.

Files:
* examples/research-sota/r12_rf_weather_eigenshift.py
* examples/research-sota/r12_rf_weather_results.json
* docs/research/sota-2026-05-22/R12-rf-weather-mapping.md
* docs/research/sota-2026-05-22/ticks/tick-5.md

docs/research/sota-2026-05-22/R12-rf-weather-mapping.md

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+# 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.

docs/research/sota-2026-05-22/ticks/tick-5.md

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+# Tick 5 — 2026-05-22 03:45 UTC
+
+**Thread:** R12 (RF weather mapping — structural drift from passive ambient WiFi)
+**Verdict:** Negative-ish result with a clearly-actionable revision path. **Honest progress.**
+
+## What shipped
+
+- `examples/research-sota/r12_rf_weather_eigenshift.py` — pure-NumPy demo that tests "can SVD-eigenvalue drift detect a synthetic structural perturbation?"
+- `examples/research-sota/r12_rf_weather_results.json` — full numbers.
+- `docs/research/sota-2026-05-22/R12-rf-weather-mapping.md` — research note covering: 10-year vision, first-experiment method, **negative result**, why it failed, three concrete revisions for next attempts (PABS / per-subcarrier residuals / multi-day baseline), what still holds, vertical applications.
+
+## Headline numbers
+
+| | Cosine distance from baseline |
+|---|---|
+| Control (no perturbation) | 0.00035 |
+| With 15% attenuation on 3 top-saliency subcarriers | 0.00024 |
+| Signal / natural-drift ratio | **0.69×** |
+
+The synthetic perturbation produced a *smaller* spectral distance than natural temporal drift from operator movement. The top-K SVD-spectrum distance approach is too coarse.
+
+## Why this is still useful
+
+1. **Saves anyone going down this path** the time of trying naive SVD-distance — the data tells us it's the wrong primitive.
+2. **Identifies the right primitives:** principal angles between subspaces (PABS), per-subcarrier residual analysis, multi-day baselines.
+3. **Cross-validates R5:** task-specific saliency (count) ≠ task-specific saliency (structure detection). Same model, same data — different relevant features. Publishable distinction.
+4. **Confirms R12 is CSI-only:** RSSI is the trace of the CSI covariance matrix; if top-10 SVD can't see this perturbation, RSSI definitely can't. Bounds R8's commercial-enablement story to counting only.
+
+## What's queued for later ticks
+
+- Implement PABS-based change detection.
+- Per-subcarrier residual time-series analysis.
+- Acquire (or simulate) multi-day data with a known structural change.
+
+## Coordination note
+
+This tick wrote NOTHING to `PROGRESS.md` to avoid races with the horizon-tracker agent (which is on the `feat/ruview-mcp-m*` track and editing PROGRESS.md concurrently). The `ticks/tick-N.md` convention used here means each cron-driven tick is fully self-contained — the final 08:00 ET summary script will consolidate them.