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wifi-densepose
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feat(adr-105): kill synthetic signal_field — only real ESP32 data left 

Continuation of ADR-105 (no synthetic outputs in production runtime).

The 20×20 SignalField heatmap was generated by mapping subcarrier
index k to angle 2π·k/N and dropping a Gaussian hotspot — a totally
fabricated spatial layout. A single sensor has no directional info
so the resulting heatmap had no correspondence to where anything
actually was in the room; UI showed believable-looking but
physically meaningless hotspots. Operator asked for boots-on-the-
ground honesty.

`generate_signal_field` now returns a zero-filled 20×1×20 grid. UI
renders blank, which is the truthful state until a real multistatic
localizer is wired (multi-AP attention from ADR-008 or the
`MultistaticFuser` already in code).

Audit of remaining fields confirmed they are either:
- already gated on real data (vital_signs returns None when br < 1 BPM,
  persons/pose_keypoints/posture/signal_quality_score all None without
  model loaded),
- or processed from real CSI (classification, features.mean_rssi,
  features.variance, enhanced_motion when multi-AP pipeline active).

`--source simulate` was already disabled by an earlier change
(exit code 2). `--pretrain` and `--train` synthetic fallbacks remain
in code as developer tools but never touch the runtime sensing path.
This commit is contained in:
arsen 2026-05-17 11:34:31 +07:00
parent 9aa027e95e
commit 30244d274b
1 changed files with 14 additions and 77 deletions
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91
v2/crates/wifi-densepose-sensing-server/src/main.rs
View File

@ -1710,86 +1710,23 @@ fn parse_esp32_frame(buf: &[u8]) -> Option<Esp32Frame> {
/// subcarriers with the highest variance produce peaks at the corresponding directions.
fn generate_signal_field(
_mean_rssi: f64,
motion_score: f64,
breathing_rate_hz: f64,
signal_quality: f64,
subcarrier_variances: &[f64],
_motion_score: f64,
_breathing_rate_hz: f64,
_signal_quality: f64,
_subcarrier_variances: &[f64],
) -> SignalField {
// ADR-105: this used to paint a 20×20 "room heatmap" by mapping each
// subcarrier index `k` to an angle `2π·k/N` and dropping a Gaussian
// hotspot at radius proportional to its variance — visually rich, but
// **physically meaningless**. A single sensor has no directional
// information, so the resulting hotspots have no correspondence to
// where anything actually is in the room. Operator requested
// boots-on-the-ground honesty: return a zero-filled grid. UI will
// render blank, which is the truthful state until a real
// multistatic localizer is wired in.
let grid = 20usize;
let mut values = vec![0.0f64; grid * grid];
let center = (grid as f64 - 1.0) / 2.0;
return SignalField { grid_size: [grid, 1, grid], values: vec![0.0; grid * grid] };
// Normalise subcarrier variances to [0, 1].
let max_var = subcarrier_variances.iter().cloned().fold(0.0f64, f64::max);
let norm_factor = if max_var > 1e-9 { max_var } else { 1.0 };
// For each cell, accumulate contributions from all subcarriers.
// Each subcarrier k is assigned an angular direction proportional to its index
// so that different subcarriers illuminate different regions of the room.
let n_sub = subcarrier_variances.len().max(1);
for (k, &var) in subcarrier_variances.iter().enumerate() {
let weight = (var / norm_factor) * motion_score;
if weight < 1e-6 {
continue;
}
// Map subcarrier index to an angle across the full 2π sweep.
let angle = (k as f64 / n_sub as f64) * 2.0 * std::f64::consts::PI;
// Place the hotspot at a distance proportional to the weight, capped at 40% of
// the grid radius so it stays within the room model.
let radius = center * 0.8 * weight.sqrt();
let hx = center + radius * angle.cos();
let hz = center + radius * angle.sin();
for z in 0..grid {
for x in 0..grid {
let dx = x as f64 - hx;
let dz = z as f64 - hz;
let dist2 = dx * dx + dz * dz;
// Gaussian blob centred on the hotspot; spread scales with weight.
let spread = (0.5 + weight * 2.0).max(0.5);
values[z * grid + x] += weight * (-dist2 / (2.0 * spread * spread)).exp();
}
}
}
// Base radial attenuation from the router assumed at grid centre.
for z in 0..grid {
for x in 0..grid {
let dx = x as f64 - center;
let dz = z as f64 - center;
let dist = (dx * dx + dz * dz).sqrt();
let base = signal_quality * (-dist * 0.12).exp();
values[z * grid + x] += base * 0.3;
}
}
// Breathing ring: if a breathing rate was estimated add a faint annular highlight
// at a radius corresponding to typical chest-wall displacement range.
if breathing_rate_hz > 0.05 {
let ring_r = center * 0.55;
let ring_width = 1.8f64;
for z in 0..grid {
for x in 0..grid {
let dx = x as f64 - center;
let dz = z as f64 - center;
let dist = (dx * dx + dz * dz).sqrt();
let ring_val = 0.08 * (-(dist - ring_r).powi(2) / (2.0 * ring_width * ring_width)).exp();
values[z * grid + x] += ring_val;
}
}
}
// Clamp and normalise to [0, 1].
let field_max = values.iter().cloned().fold(0.0f64, f64::max);
let scale = if field_max > 1e-9 { 1.0 / field_max } else { 1.0 };
for v in &mut values {
*v = (*v * scale).clamp(0.0, 1.0);
}
SignalField {
grid_size: [grid, 1, grid],
values,
}
}
// ── Feature extraction from ESP32 frame ──────────────────────────────────────