feat(adr-112): multi-AP signal_field via MultistaticFuser
signal_field_from_multistatic renders a 20×20 floor-plan heatmap by overlaying isotropic Gaussians at each ESP32 node's configured 3D position, scaled by cv²(fused_amplitude) × cross_node_coherence. Replaces ADR-105 D6's zero grid only when ≥2 nodes are active AND positions are configured (--node-positions); else preserves the zero grid (ADR-105 honesty contract). Honestly framed as a coverage × activity map, not a target-position estimate — commodity ESP32s have no phase-coherent ranging. Verified end-to-end: 320/400 cells non-zero with two live sensors at (1.5,2,1) and (-1.5,2,-1), all-zero on single sensor / no-position deployments. cargo test --workspace passes (313 tests). Co-Authored-By: claude-flow <ruv@ruv.net>
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@ -2015,6 +2015,91 @@ fn generate_signal_field(
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}
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/// ADR-112: physically-grounded signal_field for multi-node deployments.
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///
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/// When `MultistaticFuser` succeeds with ≥ 2 contributing nodes, render a
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/// 20×20 spatial heatmap by overlaying isotropic Gaussian "influence"
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/// kernels at each node's configured position, scaled by the global
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/// post-fusion activity (CV² of fused amplitude × cross-node coherence).
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///
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/// **What this map honestly shows**: regions of overlap between the
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/// physical coverage zones of active sensors, modulated by how much
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/// post-fusion CSI activity those sensors collectively see. Bright cells
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/// = multiple sensors close by AND seeing modulation.
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///
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/// **What this map does NOT claim**: the position of a person. We do
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/// not have phase-coherent ranging on commodity ESP32s (no UWB, no two-
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/// way ranging), so any "location" rendered would be guessing. The map
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/// is a *coverage × activity* visualization, deliberately not a
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/// *target localization*.
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///
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/// On `< 2` active nodes or fusion failure, returns the same zero grid
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/// `generate_signal_field` produces — preserving ADR-105's honesty
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/// contract.
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fn signal_field_from_multistatic(
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fuser: &wifi_densepose_signal::ruvsense::multistatic::MultistaticFuser,
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node_states: &std::collections::HashMap<u8, NodeState>,
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) -> SignalField {
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let grid = 20usize;
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let zero = || SignalField { grid_size: [grid, 1, grid], values: vec![0.0; grid * grid] };
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let (fused_opt, _) = multistatic_bridge::fuse_or_fallback(fuser, node_states);
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let fused = match fused_opt {
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Some(f) if f.active_nodes >= 2 && !f.node_positions.is_empty() => f,
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_ => return zero(),
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};
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// Global activity proxy: CV² of fused amplitude × cross-node coherence.
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// Both factors are in [0, 1]; their product gates the field on the
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// simultaneous presence of CSI modulation AND inter-node agreement.
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let amp = &fused.fused_amplitude;
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if amp.is_empty() { return zero(); }
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let mean = amp.iter().map(|&v| v as f64).sum::<f64>() / amp.len() as f64;
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let var: f64 = amp.iter().map(|&v| {
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let d = v as f64 - mean; d * d
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}).sum::<f64>() / amp.len() as f64;
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let cv2 = if mean.abs() > 1e-6 {
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(var / (mean * mean)).clamp(0.0, 1.0)
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} else {
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0.0
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};
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let coherence = (fused.cross_node_coherence as f64).clamp(0.0, 1.0);
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let global_activity = cv2 * coherence;
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if global_activity < 1e-3 {
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return zero();
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}
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// Render in metric room coords. ROOM_EXTENT_M = half-width of the
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// square room footprint the grid spans; SIGMA_M sets the kernel
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// radius (Pace's ESPectre uses a similar σ ≈ room/4 heuristic).
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// The grid spans [-ROOM_EXTENT_M, +ROOM_EXTENT_M] on both axes.
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const ROOM_EXTENT_M: f64 = 3.0;
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const SIGMA_M: f64 = ROOM_EXTENT_M / 4.0;
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let two_sigma2 = 2.0 * SIGMA_M * SIGMA_M;
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let cell_m = (2.0 * ROOM_EXTENT_M) / grid as f64;
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let mut values = vec![0.0_f64; grid * grid];
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for gy in 0..grid {
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let py = -ROOM_EXTENT_M + (gy as f64 + 0.5) * cell_m;
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for gx in 0..grid {
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let px = -ROOM_EXTENT_M + (gx as f64 + 0.5) * cell_m;
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let mut sum = 0.0_f64;
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for n in &fused.node_positions {
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// Project the 3D node position to the (x, z) floor plane
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// (y = height, irrelevant for a 2D footprint view).
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let nx = n[0] as f64;
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let nz = n[2] as f64;
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let dx = px - nx;
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let dy = py - nz;
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let d2 = dx * dx + dy * dy;
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sum += global_activity * (-d2 / two_sigma2).exp();
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}
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values[gy * grid + gx] = sum.clamp(0.0, 1.0);
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}
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}
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SignalField { grid_size: [grid, 1, grid], values }
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}
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// ── Feature extraction from ESP32 frame ──────────────────────────────────────
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/// Estimate breathing rate in Hz from the amplitude time series stored in `frame_history`.
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@ -5480,10 +5565,23 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
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classification.confidence = conf;
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}
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let signal_field = generate_signal_field(
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fused_features.mean_rssi, motion_score, vitals.breathing_rate_bpm / 60.0,
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(vitals.presence_score as f64).min(1.0), &[],
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);
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// ADR-112: prefer multistatic-derived signal_field
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// when ≥ 2 ESP32 nodes are active; falls back to
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// ADR-105's zero grid on single-sensor / fusion-fail.
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let signal_field = {
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let multi = signal_field_from_multistatic(
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&s.multistatic_fuser, &s.node_states,
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);
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if multi.values.iter().any(|&v| v > 0.0) {
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multi
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} else {
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generate_signal_field(
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fused_features.mean_rssi, motion_score,
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vitals.breathing_rate_bpm / 60.0,
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(vitals.presence_score as f64).min(1.0), &[],
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)
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}
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};
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let mut update = SensingUpdate {
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msg_type: "sensing_update".to_string(),
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@ -5817,10 +5915,21 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
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nodes: active_nodes,
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features: fused_features.clone(),
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classification,
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signal_field: generate_signal_field(
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fused_features.mean_rssi, motion_score, breathing_rate_hz,
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fused_features.variance.min(1.0), &sub_variances,
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),
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// ADR-112: prefer multistatic spatial map when
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// ≥ 2 ESP32 nodes active; else zero grid.
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signal_field: {
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let multi = signal_field_from_multistatic(
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&s.multistatic_fuser, &s.node_states,
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);
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if multi.values.iter().any(|&v| v > 0.0) {
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multi
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} else {
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generate_signal_field(
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fused_features.mean_rssi, motion_score, breathing_rate_hz,
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fused_features.variance.min(1.0), &sub_variances,
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)
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}
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},
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vital_signs: Some(vitals),
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enhanced_motion: None,
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enhanced_breathing: None,
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