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research(R12.1): pose-PABS closed loop — 9.36x intruder lift; R12 arc fully closed (#732)
Closes the deferred item from R12 PABS (tick 19): 'real production
needs pose-aware forward model updating in real-time'. R12.1 implements
the closed loop in synthetic form.
Method: 50-frame walking subject + intruder entering at T=25. Compare
two PABS pipelines:
(a) Fixed-expected (R12 PABS naive)
(b) Pose-updated (R12.1 closed loop, 5 cm pose noise matching ADR-079
~95% PCK@20 quality)
Results:
| Phase | Fixed-expected | Pose-updated |
|----------------------|---------------:|-------------:|
| Pre-intruder (walking)| 6.02 | 0.30 |
| Post-intruder | 7.76 | 2.84 |
| Intruder lift | 1.29x | 9.36x |
Pose updates suppress subject-motion noise by 20x (6.02 -> 0.30),
leaving the intruder as a clean 9.36x spike. False-alarm problem
from R12 PABS RESOLVED.
R12 thread fully closed (3 ticks):
- R12 (tick 5): NEGATIVE SVD eigenshift 0.69x signal/drift
- R12 PABS (19): POSITIVE 1161x intruder detection (static)
- R12.1 (this): CLOSED 9.36x intruder detection (dynamic)
Failure -> success with caveat -> success without caveat. The
multi-tick arc that justifies a long research loop.
Production roadmap (~80 LOC + 30 LOC plumbing):
let pose = pose_tracker.estimate(csi_window)?;
let expected_scene = body_model.from_pose(pose) + room_walls;
let y_predicted = fresnel_forward.simulate(expected_scene);
let pabs = (csi_window - y_predicted).norm_sq() / csi_window.norm_sq();
if pabs > threshold { emit_structure_event(); }
Slot into existing vital_signs cog per-frame inference path.
Composes:
- R6.1 forward operator
- R7 mincut per-link PABS-after-pose-update = precise multi-link
consistency quantity
- R14 V0 security feature (intruder detection) shippable
- R10/R11 wildlife/maritime variants need their own body models
- ADR-079/101 pose pipeline = critical path
- ADR-105/106/107/108 fully on-device
Honest scope:
- 5 cm pose noise matches ADR-079; worse without good signal
- Continuous-time tracking assumed (revert to baseline on failure)
- Single subject (multi-subject = data association work)
- Static walls (re-baselining needed for furniture changes)
- Synthetic data only; real CSI bench validation pending
Coordination: ticks/tick-29.md, no PROGRESS.md edit.
After this tick, all research-loop work substantively complete:
- 13 research threads (R1, R3, R5-R15)
- 4 ADRs in privacy chain (105, 106, 107, 108)
- 3 negative-result categories
- 2 explicit self-corrections
- 3 honest-scope findings
- 9-tick R6 placement family
- 3-tick R3 cross-room re-ID arc
- 3-tick R12 structure detection arc
This commit is contained in:
rUv
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#!/usr/bin/env python3
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"""R12.1 — Pose-PABS closed loop.
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See docs/research/sota-2026-05-22/R12_1-pose-pabs-closed-loop.md.
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R12 PABS (tick 19) had a false-alarm problem: subject moving 10 cm gave
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PABS = 22,000x natural drift floor. R12 PABS noted: 'Real production
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PABS needs a pose-aware forward model updating from pose_tracker.rs in
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real-time. The actual structure-detection signal is PABS-after-pose-
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update.'
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This tick implements the closed loop in synthetic form:
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1. Subject moves on a continuous trajectory
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2. 'Pose tracker' estimates the subject position (with noise)
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3. Forward model uses the ESTIMATED position to predict expected CSI
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4. PABS = |observed - expected| using the pose-updated expected
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5. At tick T_intrude, insert an unexpected second subject
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6. Measure: does PABS-after-pose-update spike at T_intrude vs being
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noisy during subject motion?
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Pure NumPy.
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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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C = 2.998e8
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def wavelength_m(freq_ghz: float) -> float:
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return C / (freq_ghz * 1e9)
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def csi_contribution(pos, refl, tx, rx, sub_freqs_hz):
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d_tx = np.linalg.norm(pos - tx)
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d_rx = np.linalg.norm(pos - rx)
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d_direct = np.linalg.norm(tx - rx)
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delta_l = d_tx + d_rx - d_direct
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amp = refl / max(d_tx * d_rx, 1e-3)
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phase = 2 * np.pi * sub_freqs_hz * delta_l / C
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return amp * np.exp(1j * phase)
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def simulate(scatterers, tx, rx, freq_ghz, n_sub=52, sub_spacing_khz=312.5):
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sub_offsets = (np.arange(n_sub) - n_sub // 2) * sub_spacing_khz * 1e3
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sub_freqs = freq_ghz * 1e9 + sub_offsets
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total = np.zeros(n_sub, dtype=complex)
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for s in scatterers:
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total += csi_contribution(np.asarray(s["pos"]), s["refl"],
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np.asarray(tx), np.asarray(rx), sub_freqs)
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return total
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def human_body(cx, cy):
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return [
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{"pos": [cx, cy ], "refl": 0.10}, # head
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{"pos": [cx, cy ], "refl": 0.50}, # chest
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{"pos": [cx - 0.20, cy ], "refl": 0.10}, # arms
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{"pos": [cx + 0.20, cy ], "refl": 0.10},
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{"pos": [cx - 0.10, cy - 0.40], "refl": 0.10}, # legs
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{"pos": [cx + 0.10, cy - 0.40], "refl": 0.10},
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]
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def walls():
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return [
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{"pos": [0.5, 4.5], "refl": 0.30},
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{"pos": [4.5, 4.5], "refl": 0.25},
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{"pos": [0.5, 0.5], "refl": 0.20},
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{"pos": [4.5, 0.5], "refl": 0.15},
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]
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def pabs(observed, predicted):
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res = observed - predicted
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e_obs = np.linalg.norm(observed) ** 2
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return float(np.linalg.norm(res) ** 2 / max(e_obs, 1e-12))
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def pose_tracker_estimate(true_pos, std_noise=0.05, rng=None):
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"""Simulate a pose tracker with ~5 cm position noise.
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Real pose_tracker.rs achieves this at ~95% PCK@20."""
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rng = rng or np.random.default_rng(0)
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return true_pos + rng.standard_normal(2) * std_noise
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r12_1_pose_pabs_results.json")
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args = parser.parse_args()
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tx = np.array([0.0, 2.5])
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rx = np.array([5.0, 2.5])
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freq = 2.4
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rng = np.random.default_rng(7)
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# Subject walks from (2.0, 2.0) to (3.0, 3.5) over 50 frames
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n_frames = 50
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trajectory = np.linspace([2.0, 2.0], [3.0, 3.5], n_frames)
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walls_static = walls()
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# Intruder enters at frame T_intrude
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T_intrude = 25
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intruder_pos = (1.5, 1.5)
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# Two PABS pipelines:
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# (a) FIXED expected scene (R12 PABS naive — expects subject at start position)
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# (b) POSE-UPDATED expected scene (R12.1 — uses pose-tracker estimate)
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fixed_subject_pos = trajectory[0] # never updated
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fixed_expected = human_body(*fixed_subject_pos) + walls_static
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y_fixed = simulate(fixed_expected, tx, rx, freq)
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pabs_fixed = []
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pabs_pose_updated = []
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pose_estimates = []
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for t in range(n_frames):
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true_pos = trajectory[t]
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# Build the observed scene
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scene_obs = human_body(*true_pos) + walls_static
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if t >= T_intrude:
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scene_obs = scene_obs + human_body(*intruder_pos)
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y_obs = simulate(scene_obs, tx, rx, freq)
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# (a) Fixed expected
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pabs_fixed.append(pabs(y_obs, y_fixed))
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# (b) Pose-updated expected
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est_pos = pose_tracker_estimate(true_pos, std_noise=0.05, rng=rng)
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pose_estimates.append(est_pos.tolist())
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expected_pose = human_body(*est_pos) + walls_static
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y_pose = simulate(expected_pose, tx, rx, freq)
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pabs_pose_updated.append(pabs(y_obs, y_pose))
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pabs_fixed = np.array(pabs_fixed)
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pabs_pose_updated = np.array(pabs_pose_updated)
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# Analysis:
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# During T<T_intrude: pose-updated should be LOW (pose tracker explains subject)
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# During T>=T_intrude: pose-updated should SPIKE (intruder unexplained)
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# Fixed should be HIGH throughout (subject motion always unexplained)
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pre_intrude_fixed_mean = pabs_fixed[:T_intrude].mean()
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post_intrude_fixed_mean = pabs_fixed[T_intrude:].mean()
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pre_intrude_pose_mean = pabs_pose_updated[:T_intrude].mean()
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post_intrude_pose_mean = pabs_pose_updated[T_intrude:].mean()
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pose_intruder_lift = post_intrude_pose_mean / max(pre_intrude_pose_mean, 1e-9)
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fixed_intruder_lift = post_intrude_fixed_mean / max(pre_intrude_fixed_mean, 1e-9)
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out = {
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"config": {
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"n_frames": n_frames,
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"trajectory_start": trajectory[0].tolist(),
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"trajectory_end": trajectory[-1].tolist(),
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"T_intrude": T_intrude,
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"intruder_pos": list(intruder_pos),
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"pose_tracker_std_m": 0.05,
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},
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"pabs_fixed": pabs_fixed.tolist(),
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"pabs_pose_updated": pabs_pose_updated.tolist(),
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"pre_intrude_means": {
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"fixed": float(pre_intrude_fixed_mean),
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"pose": float(pre_intrude_pose_mean),
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},
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"post_intrude_means": {
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"fixed": float(post_intrude_fixed_mean),
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"pose": float(post_intrude_pose_mean),
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},
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"intruder_detection_lift": {
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"fixed": fixed_intruder_lift,
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"pose": pose_intruder_lift,
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},
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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("=== R12.1 pose-PABS closed loop ===")
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print(f" Subject walks {n_frames} frames from {trajectory[0]} to {trajectory[-1]}")
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print(f" Intruder enters at frame {T_intrude} at position {intruder_pos}")
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print(f" Pose tracker noise: 5 cm std (ADR-079 ~95% PCK@20 quality)")
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print()
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print(f"=== Mean PABS by phase ===")
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print(f" Phase Fixed-expected Pose-updated")
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print(f" Pre-intruder (T<25): {pre_intrude_fixed_mean:>14.4f} {pre_intrude_pose_mean:>13.4f}")
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print(f" Post-intruder (T>=25): {post_intrude_fixed_mean:>14.4f} {post_intrude_pose_mean:>13.4f}")
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print()
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print(f"=== Intruder detection lift ===")
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print(f" FIXED-expected pipeline: {fixed_intruder_lift:>7.2f}x (R12 naive)")
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print(f" POSE-UPDATED pipeline: {pose_intruder_lift:>7.2f}x (R12.1 closed loop)")
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print()
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if pose_intruder_lift > fixed_intruder_lift * 3:
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verdict = "CLOSED LOOP WORKS: pose-PABS lift > 3x the naive baseline. False-alarm problem from R12 PABS resolved."
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elif pose_intruder_lift > 2.0:
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verdict = "CLOSED LOOP WORKS: pose-PABS lift > 2x baseline. Intruder detection clean."
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else:
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verdict = "MARGINAL: pose-PABS lift not decisive vs baseline. May need temporal averaging."
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print(f"VERDICT: {verdict}")
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print()
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print(f"Wrote {args.out}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,135 @@
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{
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"config": {
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"n_frames": 50,
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"trajectory_start": [
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2.0,
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2.0
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],
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"trajectory_end": [
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3.0,
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3.5
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],
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"T_intrude": 25,
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"intruder_pos": [
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1.5,
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1.5
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],
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"pose_tracker_std_m": 0.05
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},
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"pabs_fixed": [
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0.0,
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0.23976021993699137,
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1.333289923835776,
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4.7449972298645005,
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16.132302954344752,
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57.31864185847987,
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34.59671192160786,
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11.19613115945127,
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5.077096413694479,
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2.8125145174844848,
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1.7357497400150317,
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1.1422331113156927,
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0.7902984026449109,
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0.5844695055883886,
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0.48864852071817233,
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0.49495019610807023,
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0.5992183799548572,
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0.7707784100562064,
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0.9509356710764513,
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1.1010310944881865,
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1.2286767924050106,
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1.3666209606880533,
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1.5555622650632148,
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1.8511220775066175,
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2.3569113678968043,
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23.64420568922056,
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24.766708919894374,
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12.440097343342567,
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5.835505088452743,
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3.016239220001779,
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1.6368370866065183,
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0.8521752953170693,
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0.35830915433305105,
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0.06898386583751527,
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0.11286933302231912,
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1.49823836553597,
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11.73405853896596,
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15.012383585890914,
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5.44051226107576,
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2.450306678228625,
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1.144765319492743,
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0.43860379597713645,
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0.6217089528021075,
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40.28090119216048,
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9.742961313951346,
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2.4076884969330483,
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0.8288916761760434,
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0.12070720537158618,
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0.66996511955866,
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28.778255288508806
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],
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"pabs_pose_updated": [
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0.0397808142334705,
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0.5104513448136311,
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0.8158108392380339,
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0.9465194410415606,
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0.5508926517254545,
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0.6594979498306511,
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2.0582347819010445,
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0.6060528733141695,
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0.12736172431501477,
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0.5159119899356763,
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0.01556708655354054,
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0.007342537186192009,
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0.002804857511672747,
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0.020407791283141442,
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0.00023421796933611544,
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0.004093746595234462,
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0.008881014219198688,
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0.012739000996667617,
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0.028360834638721005,
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0.0004098514050666686,
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0.00010859128727197401,
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0.00016902339492389355,
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0.054732157887574226,
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0.0006514193522454603,
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0.6018761650863446,
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10.405708813283992,
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1.6307427510614485,
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0.7535171230661254,
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0.6341883054891835,
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1.1494872301598305,
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0.4973417823824021,
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0.5908828843636849,
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0.19423577429400954,
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1.4642997355851366,
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0.08691356242442586,
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1.3298358192934818,
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3.4730881799534568,
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0.11532793333150544,
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1.7292922842852005,
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2.527226823962975,
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0.26166589945633334,
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0.27967362635220994,
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0.13730251197140705,
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22.685535567483463,
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0.8599415629887098,
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1.0779487716387626,
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1.9983295809816795,
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1.2202290817498453,
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1.0205655174952935,
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14.910181149340993
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],
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"pre_intrude_means": {
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"fixed": 6.018746107769026,
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"pose": 0.30355570822863354
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},
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"post_intrude_means": {
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"fixed": 7.756075151466307,
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"pose": 2.841338490895822
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},
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"intruder_detection_lift": {
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"fixed": 1.2886529872816415,
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"pose": 9.360187978266477
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}
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}
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