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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
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#!/usr/bin/env python3
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"""R12 — RF weather: can SVD-eigenvalue drift detect structural changes?
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See docs/research/sota-2026-05-22/R12-rf-weather-mapping.md.
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The persistent-room field model in `wifi-densepose-signal/src/ruvsense/
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field_model.rs` does an SVD on empty-room CSI to extract an eigenstructure
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that describes "what this room's RF reflection looks like with nobody
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in it". Today that's used to subtract the room's baseline so motion
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detection isn't confused by static multipath.
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This experiment asks a different question: **does the eigenvalue
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*spectrum* itself drift in a detectable way when something structural
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changes in the room?** "Structural change" = a new piece of furniture,
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a window that opened, water in the wall, settled foundation, missing
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ceiling tile. The 10-year vision (R12 research note) is continuous
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building-integrity monitoring from passive ambient WiFi.
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Test:
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1. Take the existing 1,077 CSI windows. Split first 50% = "before",
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last 50% = "after".
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2. Inject a synthetic "structural perturbation" into the "after"
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half — multiply 3 subcarriers by 0.85 (simulating a new reflective
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surface that attenuates those frequencies).
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3. For each half, stack the windows into a `[N, 56]` per-frame
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matrix (each row = one timestep), compute SVD, take the top-10
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singular values.
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4. Measure: do the singular-value spectra differ in a way that
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distinguishes "structural perturbation present" from "no
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perturbation"?
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5. Repeat with NO perturbation as control — the same first-half /
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second-half split should produce *similar* spectra (just temporal
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drift from operator movement, not structural).
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If the perturbed-vs-control eigenvalue spectra are distinguishable by
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a simple distance metric, RF-weather detection is feasible.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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N_SUB, N_FRAMES = 56, 20
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def load_windows(path: Path, max_samples: int | None = None) -> np.ndarray:
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csis = []
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with path.open(encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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d = json.loads(line)
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shape = d.get("csi_shape", [N_SUB, N_FRAMES])
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if shape != [N_SUB, N_FRAMES]:
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continue
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csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
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csis.append(csi)
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if max_samples and len(csis) >= max_samples:
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break
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return np.stack(csis)
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def perturb_subcarriers(X: np.ndarray, indices: list[int], gain: float) -> np.ndarray:
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"""Multiply the listed subcarriers by `gain` to simulate a structural
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change (e.g. a new reflector attenuates certain frequencies)."""
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out = X.copy()
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out[:, indices, :] *= gain
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return out
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def per_frame_matrix(X: np.ndarray) -> np.ndarray:
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"""Stack all windows' frames into a [N_total_frames, 56] matrix.
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Each row is one timestep, used as a multivariate observation of the
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56-subcarrier channel state."""
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return X.transpose(0, 2, 1).reshape(-1, N_SUB)
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def top_k_singular_values(M: np.ndarray, k: int = 10) -> np.ndarray:
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"""Compute SVD on M, return top-k singular values."""
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M_centered = M - M.mean(axis=0, keepdims=True)
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# Use SVD on the centered matrix (== PCA without normalisation)
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s = np.linalg.svd(M_centered, compute_uv=False)
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return s[:k]
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def spectrum_distance(s1: np.ndarray, s2: np.ndarray) -> float:
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"""Cosine distance between two singular-value spectra. 0 = identical
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direction, 2 = opposite. Symmetric, scale-invariant."""
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s1n = s1 / (np.linalg.norm(s1) + 1e-9)
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s2n = s2 / (np.linalg.norm(s2) + 1e-9)
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return float(1.0 - np.dot(s1n, s2n))
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--paired", required=True)
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parser.add_argument("--out", default="examples/research-sota/r12_rf_weather_results.json")
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parser.add_argument("--perturb-indices", default="30,41,52",
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help="comma-separated subcarrier indices to perturb (chosen from R5's top-saliency list)")
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parser.add_argument("--perturb-gain", type=float, default=0.85)
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args = parser.parse_args()
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print(f"Loading windows from {args.paired}")
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X = load_windows(Path(args.paired))
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print(f" total windows: {X.shape[0]} (shape {X.shape})")
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n = X.shape[0]
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half = n // 2
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X_before = X[:half]
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X_after_raw = X[half:] # unmodified second half — the CONTROL
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perturb_idx = [int(x) for x in args.perturb_indices.split(",")]
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X_after_perturbed = perturb_subcarriers(X_after_raw, perturb_idx, args.perturb_gain)
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# Convert each half to a [N_frames, 56] matrix
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M_before = per_frame_matrix(X_before)
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M_after_raw = per_frame_matrix(X_after_raw)
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M_after_pert = per_frame_matrix(X_after_perturbed)
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print(f" per-frame matrix: before={M_before.shape}, after={M_after_raw.shape}")
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# Top-10 singular values per half
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s_before = top_k_singular_values(M_before, k=10)
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s_after_raw = top_k_singular_values(M_after_raw, k=10)
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s_after_pert = top_k_singular_values(M_after_pert, k=10)
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print(f"\n Singular value spectra (top-10):")
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print(f" before : [{', '.join(f'{v:.1f}' for v in s_before)}]")
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print(f" after (raw) : [{', '.join(f'{v:.1f}' for v in s_after_raw)}]")
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print(f" after (pert) : [{', '.join(f'{v:.1f}' for v in s_after_pert)}]")
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# Distances
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d_raw = spectrum_distance(s_before, s_after_raw)
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d_pert = spectrum_distance(s_before, s_after_pert)
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print(f"\n Cosine distances from BEFORE:")
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print(f" before -> after raw (control, no perturbation): {d_raw:.5f}")
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print(f" before -> after pert (synthetic structural shift): {d_pert:.5f}")
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# Distance ratio = how much the perturbation amplifies the detection signal
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# over the natural temporal drift.
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if d_raw > 1e-9:
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ratio = d_pert / d_raw
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print(f"\n Signal-to-natural-drift ratio: {ratio:.2f}x")
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if d_pert > d_raw * 3:
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verdict = "STRONG: perturbation easily distinguishable from natural temporal drift"
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elif d_pert > d_raw * 1.5:
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verdict = "MODERATE: perturbation detectable but with margin"
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else:
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verdict = "WEAK: structural perturbation gets lost in temporal drift"
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print(f"\n Verdict: {verdict}")
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out = {
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"perturbation": {
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"subcarrier_indices": perturb_idx,
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"amplitude_gain": args.perturb_gain,
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"comment": "simulates a new reflective surface that attenuates these frequencies",
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},
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"n_before_windows": int(half),
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"n_after_windows": int(n - half),
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"spectra": {
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"before": s_before.tolist(),
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"after_raw_control": s_after_raw.tolist(),
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"after_perturbed": s_after_pert.tolist(),
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},
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"distances": {
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"before_to_after_raw": d_raw,
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"before_to_after_perturbed": d_pert,
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"signal_over_natural_drift": float(d_pert / max(d_raw, 1e-9)),
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},
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"verdict": verdict,
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}
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Path(args.out).parent.mkdir(parents=True, exist_ok=True)
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Path(args.out).write_text(json.dumps(out, indent=2))
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print(f"\nWrote {args.out}")
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if __name__ == "__main__":
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main()
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{
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"perturbation": {
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"subcarrier_indices": [
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30,
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41,
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52
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],
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"amplitude_gain": 0.85,
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"comment": "simulates a new reflective surface that attenuates these frequencies"
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},
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"n_before_windows": 538,
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"n_after_windows": 539,
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"spectra": {
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"before": [
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2220.65673828125,
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1856.8695068359375,
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1563.7314453125,
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1303.56298828125,
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1057.757080078125,
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770.67822265625,
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757.5601196289062,
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689.5866088867188,
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595.6748046875,
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556.3777465820312
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],
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"after_raw_control": [
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2182.5712890625,
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1837.5084228515625,
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1647.6357421875,
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1315.103759765625,
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1053.489013671875,
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794.1417236328125,
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737.1859130859375,
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704.1968994140625,
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571.363037109375,
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535.6047973632812
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],
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"after_perturbed": [
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2172.6552734375,
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1824.164794921875,
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1615.7850341796875,
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1304.227783203125,
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1040.461181640625,
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791.2919921875,
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736.2902221679688,
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691.3584594726562,
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568.5400390625,
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530.7666625976562
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]
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},
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"distances": {
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"before_to_after_raw": 0.0003509521484375,
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"before_to_after_perturbed": 0.00024056434631347656,
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"signal_over_natural_drift": 0.6854619565217391
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},
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"verdict": "WEAK: structural perturbation gets lost in temporal drift"
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}
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