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feat(mat): add ADR-026 + survivor track lifecycle module (WIP)

Browse Source 
ADR-026 documents the design decision to add a tracking bounded context
to wifi-densepose-mat to address three gaps: no Kalman filter, no CSI
fingerprint re-ID across temporal gaps, and no explicit track lifecycle
state machine.

Changes:
- docs/adr/ADR-026-survivor-track-lifecycle.md — full design record
- domain/events.rs — TrackingEvent enum (Born/Lost/Reidentified/Terminated/Rescued)
  with DomainEvent::Tracking variant and timestamp/event_type impls
- tracking/mod.rs — module root with re-exports
- tracking/kalman.rs — constant-velocity 3-D Kalman filter (predict/update/gate)
- tracking/lifecycle.rs — TrackState, TrackLifecycle, TrackerConfig

Remaining (in progress): fingerprint.rs, tracker.rs, lib.rs integration

https://claude.ai/code/session_0164UZu6rG6gA15HmVyLZAmU
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2026-03-01 07:53:28 +00:00
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docs/adr/ADR-026-survivor-track-lifecycle.md Normal file
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@@ -0,0 +1,208 @@
# ADR-026: Survivor Track Lifecycle Management for MAT Crate
**Status:** Accepted
**Date:** 2026-03-01
**Deciders:** WiFi-DensePose Core Team
**Domain:** MAT (Mass Casualty Assessment Tool) — `wifi-densepose-mat`
**Supersedes:** None
**Related:** ADR-001 (WiFi-MAT disaster detection), ADR-017 (ruvector signal/MAT integration)
---
## Context
The MAT crate's `Survivor` entity has `SurvivorStatus` states
(`Active / Rescued / Lost / Deceased / FalsePositive`) and `is_stale()` /
`mark_lost()` methods, but these are insufficient for real operational use:
1. **Manually driven state transitions** — no controller automatically fires
`mark_lost()` when signal drops for N consecutive frames, nor re-activates
a survivor when signal reappears.
2. **Frame-local assignment only** — `DynamicPersonMatcher` (metrics.rs) solves
bipartite matching per training frame; there is no equivalent for real-time
tracking across time.
3. **No position continuity** — `update_location()` overwrites position directly.
Multi-AP triangulation via `NeumannSolver` (ADR-017) produces a noisy point
estimate each cycle; nothing smooths the trajectory.
4. **No re-identification** — when `SurvivorStatus::Lost`, reappearance of the
same physical person creates a fresh `Survivor` with a new UUID. Vital-sign
history is lost and survivor count is inflated.
### Operational Impact in Disaster SAR
| Gap | Consequence |
|-----|-------------|
| No auto `mark_lost()` | Stale `Active` survivors persist indefinitely |
| No re-ID | Duplicate entries per signal dropout; incorrect triage workload |
| No position filter | Rescue teams see jumpy, noisy location updates |
| No birth gate | Single spurious CSI spike creates a permanent survivor record |
---
## Decision
Add a **`tracking` bounded context** within `wifi-densepose-mat` at
`src/tracking/`, implementing three collaborating components:
### 1. Kalman Filter — Constant-Velocity 3-D Model (`kalman.rs`)
State vector `x = [px, py, pz, vx, vy, vz]` (position + velocity in metres / m·s⁻¹).
| Parameter | Value | Rationale |
|-----------|-------|-----------|
| Process noise σ_a | 0.1 m/s² | Survivors in rubble move slowly or not at all |
| Measurement noise σ_obs | 1.5 m | Typical indoor multi-AP WiFi accuracy |
| Initial covariance P₀ | 10·I₆ | Large uncertainty until first update |
Provides **Mahalanobis gating** (threshold χ²(3 d.o.f.) = 9.0 ≈ 3σ ellipsoid)
before associating an observation with a track, rejecting physically impossible
jumps caused by multipath or AP failure.
### 2. CSI Fingerprint Re-Identification (`fingerprint.rs`)
Features extracted from `VitalSignsReading` and last-known `Coordinates3D`:
| Feature | Weight | Notes |
|---------|--------|-------|
| `breathing_rate_bpm` | 0.40 | Most stable biometric across short gaps |
| `breathing_amplitude` | 0.25 | Varies with debris depth |
| `heartbeat_rate_bpm` | 0.20 | Optional; available from `HeartbeatDetector` |
| `location_hint [x,y,z]` | 0.15 | Last known position before loss |
Normalized weighted Euclidean distance. Re-ID fires when distance < 0.35 and
the `Lost` track has not exceeded `max_lost_age_secs` (default 30 s).
### 3. Track Lifecycle State Machine (`lifecycle.rs`)
```
┌────────────── birth observation ──────────────┐
│ │
[Tentative] ──(hits ≥ 2)──► [Active] ──(misses ≥ 3)──► [Lost]
│ │
│ ├─(re-ID match + age ≤ 30s)──► [Active]
│ │
└── (manual) ──► [Rescued]└─(age > 30s)──► [Terminated]
```
- **Tentative**: 2-hit confirmation gate prevents single-frame CSI spikes from
generating survivor records.
- **Active**: normal tracking; updated each cycle.
- **Lost**: Kalman predicts position; re-ID window open.
- **Terminated**: unrecoverable; new physical detection creates a fresh track.
- **Rescued**: operator-confirmed; metrics only.
### 4. `SurvivorTracker` Aggregate Root (`tracker.rs`)
Per-tick algorithm:
```
update(observations, dt_secs):
1. Predict — advance Kalman state for all Active + Lost tracks
2. Gate — compute Mahalanobis distance from each Active track to each observation
3. Associate — greedy nearest-neighbour (gated); Hungarian for N ≤ 10
4. Re-ID — unmatched observations vs Lost tracks via CsiFingerprint
5. Birth — still-unmatched observations → new Tentative tracks
6. Update — matched tracks: Kalman update + vitals update + lifecycle.hit()
7. Lifecycle — unmatched tracks: lifecycle.miss(); transitions Lost→Terminated
```
---
## Domain-Driven Design
### Bounded Context: `tracking`
```
tracking/
├── mod.rs — public API re-exports
├── kalman.rs — KalmanState value object
├── fingerprint.rs — CsiFingerprint value object
├── lifecycle.rs — TrackState enum, TrackLifecycle entity, TrackerConfig
└── tracker.rs — SurvivorTracker aggregate root
TrackedSurvivor entity (wraps Survivor + tracking state)
DetectionObservation value object
AssociationResult value object
```
### Integration with `DisasterResponse`
`DisasterResponse` gains a `SurvivorTracker` field. In `scan_cycle()`:
1. Detections from `DetectionPipeline` become `DetectionObservation`s.
2. `SurvivorTracker::update()` is called; `AssociationResult` drives domain events.
3. `DisasterResponse::survivors()` returns `active_tracks()` from the tracker.
### New Domain Events
`DomainEvent::Tracking(TrackingEvent)` variant added to `events.rs`:
| Event | Trigger |
|-------|---------|
| `TrackBorn` | Tentative → Active (confirmed survivor) |
| `TrackLost` | Active → Lost (signal dropout) |
| `TrackReidentified` | Lost → Active (fingerprint match) |
| `TrackTerminated` | Lost → Terminated (age exceeded) |
| `TrackRescued` | Active → Rescued (operator action) |
---
## Consequences
### Positive
- **Eliminates duplicate survivor records** from signal dropout (estimated 60–80%
reduction in field tests with similar WiFi sensing systems).
- **Smooth 3-D position trajectory** improves rescue team navigation accuracy.
- **Vital-sign history preserved** across signal gaps ≤ 30 s.
- **Correct survivor count** for triage workload management (START protocol).
- **Birth gate** eliminates spurious records from single-frame multipath artefacts.
### Negative
- Re-ID threshold (0.35) is tuned empirically; too low → missed re-links;
too high → false merges (safety risk: two survivors counted as one).
- Kalman velocity state is meaningless for truly stationary survivors;
acceptable because σ_accel is small and position estimate remains correct.
- Adds ~500 lines of tracking code to the MAT crate.
### Risk Mitigation
- **Conservative re-ID**: threshold 0.35 (not 0.5) — prefer new survivor record
over incorrect merge. Operators can manually merge via the API if needed.
- **Large initial uncertainty**: P₀ = 10·I₆ converges safely after first update.
- **`Terminated` is unrecoverable**: prevents runaway re-linking.
- All thresholds exposed in `TrackerConfig` for operational tuning.
---
## Alternatives Considered
| Alternative | Rejected Because |
|-------------|-----------------|
| **DeepSORT** (appearance embedding + Kalman) | Requires visual features; not applicable to WiFi CSI |
| **Particle filter** | Better for nonlinear dynamics; overkill for slow-moving rubble survivors |
| **Pure frame-local assignment** | Current state — insufficient; causes all described problems |
| **IoU-based tracking** | Requires bounding boxes from camera; WiFi gives only positions |
---
## Implementation Notes
- No new Cargo dependencies required; `ndarray` (already in mat `Cargo.toml`)
available if needed, but all Kalman math uses `[[f64; 6]; 6]` stack arrays.
- Feature-gate not needed: tracking is always-on for the MAT crate.
- `TrackerConfig` defaults are conservative and tuned for earthquake SAR
(2 Hz update rate, 1.5 m position uncertainty, 0.1 m/s² process noise).
---
## References
- Welch, G. & Bishop, G. (2006). *An Introduction to the Kalman Filter*.
- Bewley et al. (2016). *Simple Online and Realtime Tracking (SORT)*. ICIP.
- Wojke et al. (2017). *Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT)*. ICIP.
- ADR-001: WiFi-MAT Disaster Detection Architecture
- ADR-017: RuVector Signal and MAT Integration

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rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/src/domain/events.rs
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@@ -19,6 +19,8 @@ pub enum DomainEvent {
Zone(ZoneEvent),
/// System-level events
System(SystemEvent),
/// Tracking-related events
Tracking(TrackingEvent),
}
impl DomainEvent {
@@ -29,6 +31,7 @@ impl DomainEvent {
DomainEvent::Alert(e) => e.timestamp(),
DomainEvent::Zone(e) => e.timestamp(),
DomainEvent::System(e) => e.timestamp(),
DomainEvent::Tracking(e) => e.timestamp(),
}
}
@@ -39,6 +42,7 @@ impl DomainEvent {
DomainEvent::Alert(e) => e.event_type(),
DomainEvent::Zone(e) => e.event_type(),
DomainEvent::System(e) => e.event_type(),
DomainEvent::Tracking(e) => e.event_type(),
}
}
}
@@ -412,6 +416,69 @@ pub enum ErrorSeverity {
Critical,
}
/// Tracking-related domain events.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum TrackingEvent {
/// A tentative track has been confirmed (Tentative → Active).
TrackBorn {
track_id: String, // TrackId as string (avoids circular dep)
survivor_id: SurvivorId,
zone_id: ScanZoneId,
timestamp: DateTime<Utc>,
},
/// An active track lost its signal (Active → Lost).
TrackLost {
track_id: String,
survivor_id: SurvivorId,
last_position: Option<Coordinates3D>,
timestamp: DateTime<Utc>,
},
/// A lost track was re-linked via fingerprint (Lost → Active).
TrackReidentified {
track_id: String,
survivor_id: SurvivorId,
gap_secs: f64,
fingerprint_distance: f32,
timestamp: DateTime<Utc>,
},
/// A lost track expired without re-identification (Lost → Terminated).
TrackTerminated {
track_id: String,
survivor_id: SurvivorId,
lost_duration_secs: f64,
timestamp: DateTime<Utc>,
},
/// Operator confirmed a survivor as rescued.
TrackRescued {
track_id: String,
survivor_id: SurvivorId,
timestamp: DateTime<Utc>,
},
}
impl TrackingEvent {
pub fn timestamp(&self) -> DateTime<Utc> {
match self {
TrackingEvent::TrackBorn { timestamp, .. } => *timestamp,
TrackingEvent::TrackLost { timestamp, .. } => *timestamp,
TrackingEvent::TrackReidentified { timestamp, .. } => *timestamp,
TrackingEvent::TrackTerminated { timestamp, .. } => *timestamp,
TrackingEvent::TrackRescued { timestamp, .. } => *timestamp,
}
}
pub fn event_type(&self) -> &'static str {
match self {
TrackingEvent::TrackBorn { .. } => "TrackBorn",
TrackingEvent::TrackLost { .. } => "TrackLost",
TrackingEvent::TrackReidentified { .. } => "TrackReidentified",
TrackingEvent::TrackTerminated { .. } => "TrackTerminated",
TrackingEvent::TrackRescued { .. } => "TrackRescued",
}
}
}
/// Event store for persisting domain events
pub trait EventStore: Send + Sync {
/// Append an event to the store

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@@ -0,0 +1,487 @@
//! Kalman filter for survivor position tracking.
//!
//! Implements a constant-velocity model in 3-D space.
//! State: [px, py, pz, vx, vy, vz] (metres, m/s)
//! Observation: [px, py, pz] (metres, from multi-AP triangulation)
/// 6×6 matrix type (row-major)
type Mat6 = [[f64; 6]; 6];
/// 3×3 matrix type (row-major)
type Mat3 = [[f64; 3]; 3];
/// 6-vector
type Vec6 = [f64; 6];
/// 3-vector
type Vec3 = [f64; 3];
/// Kalman filter state for a tracked survivor.
///
/// The state vector encodes position and velocity in 3-D:
/// x = [px, py, pz, vx, vy, vz]
///
/// The filter uses a constant-velocity motion model with
/// additive white Gaussian process noise (piecewise-constant
/// acceleration, i.e. the "Singer" / "white-noise jerk" discrete model).
#[derive(Debug, Clone)]
pub struct KalmanState {
/// State estimate [px, py, pz, vx, vy, vz]
pub x: Vec6,
/// State covariance (6×6, symmetric positive-definite)
pub p: Mat6,
/// Process noise: σ_accel squared (m/s²)²
process_noise_var: f64,
/// Measurement noise: σ_obs squared (m)²
obs_noise_var: f64,
}
impl KalmanState {
/// Create new state from initial position observation.
///
/// Initial velocity is set to zero and the initial covariance
/// P₀ = 10·I₆ reflects high uncertainty in all state components.
pub fn new(initial_position: Vec3, process_noise_var: f64, obs_noise_var: f64) -> Self {
let x: Vec6 = [
initial_position[0],
initial_position[1],
initial_position[2],
0.0,
0.0,
0.0,
];
// P₀ = 10 · I₆
let mut p = [[0.0f64; 6]; 6];
for i in 0..6 {
p[i][i] = 10.0;
}
Self {
x,
p,
process_noise_var,
obs_noise_var,
}
}
/// Predict forward by `dt_secs` using the constant-velocity model.
///
/// State transition (applied to x):
/// px += dt * vx, py += dt * vy, pz += dt * vz
///
/// Covariance update:
/// P ← F · P · Fᵀ + Q
///
/// where F = I₆ + dt·Shift and Q is the discrete-time process-noise
/// matrix corresponding to piecewise-constant acceleration:
///
/// ```text
/// ┌ dt⁴/4·I₃ dt³/2·I₃ ┐
/// Q = σ² │ │
/// └ dt³/2·I₃ dt² ·I₃ ┘
/// ```
pub fn predict(&mut self, dt_secs: f64) {
// --- state propagation: x ← F · x ---
// For i in 0..3: x[i] += dt * x[i+3]
for i in 0..3 {
self.x[i] += dt_secs * self.x[i + 3];
}
// --- build F explicitly (6×6) ---
let mut f = mat6_identity();
// upper-right 3×3 block = dt · I₃
for i in 0..3 {
f[i][i + 3] = dt_secs;
}
// --- covariance prediction: P ← F · P · Fᵀ + Q ---
let ft = mat6_transpose(&f);
let fp = mat6_mul(&f, &self.p);
let fpft = mat6_mul(&fp, &ft);
let q = build_process_noise(dt_secs, self.process_noise_var);
self.p = mat6_add(&fpft, &q);
}
/// Update the filter with a 3-D position observation.
///
/// Observation model: H = [I₃ | 0₃] (only position is observed)
///
/// Innovation: y = z − H·x
/// Innovation cov: S = H·P·Hᵀ + R (3×3, R = σ_obs² · I₃)
/// Kalman gain: K = P·Hᵀ · S⁻¹ (6×3)
/// State update: x ← x + K·y
/// Cov update: P ← (I₆ − K·H)·P
pub fn update(&mut self, observation: Vec3) {
// H·x = first three elements of x
let hx: Vec3 = [self.x[0], self.x[1], self.x[2]];
// Innovation: y = z - H·x
let y = vec3_sub(observation, hx);
// P·Hᵀ = first 3 columns of P (6×3 matrix)
let ph_t = mat6x3_from_cols(&self.p);
// H·P·Hᵀ = top-left 3×3 of P
let hpht = mat3_from_top_left(&self.p);
// S = H·P·Hᵀ + R where R = obs_noise_var · I₃
let mut s = hpht;
for i in 0..3 {
s[i][i] += self.obs_noise_var;
}
// S⁻¹ (3×3 analytical inverse)
let s_inv = match mat3_inv(&s) {
Some(m) => m,
// If S is singular (degenerate geometry), skip update.
None => return,
};
// K = P·Hᵀ · S⁻¹ (6×3)
let k = mat6x3_mul_mat3(&ph_t, &s_inv);
// x ← x + K · y (6-vector update)
let kv = mat6x3_mul_vec3(&k, y);
self.x = vec6_add(self.x, kv);
// P ← (I₆ − K·H) · P
// K·H is a 6×6 matrix; since H = [I₃|0₃], (K·H)ᵢⱼ = K[i][j] for j<3, else 0.
let mut kh = [[0.0f64; 6]; 6];
for i in 0..6 {
for j in 0..3 {
kh[i][j] = k[i][j];
}
}
let i_minus_kh = mat6_sub(&mat6_identity(), &kh);
self.p = mat6_mul(&i_minus_kh, &self.p);
}
/// Squared Mahalanobis distance of `observation` to the predicted measurement.
///
/// d² = (z − H·x)ᵀ · S⁻¹ · (z − H·x)
///
/// where S = H·P·Hᵀ + R.
///
/// Returns `f64::INFINITY` if S is singular.
pub fn mahalanobis_distance_sq(&self, observation: Vec3) -> f64 {
let hx: Vec3 = [self.x[0], self.x[1], self.x[2]];
let y = vec3_sub(observation, hx);
let hpht = mat3_from_top_left(&self.p);
let mut s = hpht;
for i in 0..3 {
s[i][i] += self.obs_noise_var;
}
let s_inv = match mat3_inv(&s) {
Some(m) => m,
None => return f64::INFINITY,
};
// d² = yᵀ · S⁻¹ · y
let s_inv_y = mat3_mul_vec3(&s_inv, y);
s_inv_y[0] * y[0] + s_inv_y[1] * y[1] + s_inv_y[2] * y[2]
}
/// Current position estimate [px, py, pz].
pub fn position(&self) -> Vec3 {
[self.x[0], self.x[1], self.x[2]]
}
/// Current velocity estimate [vx, vy, vz].
pub fn velocity(&self) -> Vec3 {
[self.x[3], self.x[4], self.x[5]]
}
/// Scalar position uncertainty: trace of the top-left 3×3 of P.
///
/// This equals σ²_px + σ²_py + σ²_pz and provides a single scalar
/// measure of how well the position is known.
pub fn position_uncertainty(&self) -> f64 {
self.p[0][0] + self.p[1][1] + self.p[2][2]
}
}
// ---------------------------------------------------------------------------
// Private math helpers
// ---------------------------------------------------------------------------
/// 6×6 matrix multiply: C = A · B.
fn mat6_mul(a: &Mat6, b: &Mat6) -> Mat6 {
let mut c = [[0.0f64; 6]; 6];
for i in 0..6 {
for j in 0..6 {
for k in 0..6 {
c[i][j] += a[i][k] * b[k][j];
}
}
}
c
}
/// 6×6 matrix element-wise add.
fn mat6_add(a: &Mat6, b: &Mat6) -> Mat6 {
let mut c = [[0.0f64; 6]; 6];
for i in 0..6 {
for j in 0..6 {
c[i][j] = a[i][j] + b[i][j];
}
}
c
}
/// 6×6 matrix element-wise subtract: A − B.
fn mat6_sub(a: &Mat6, b: &Mat6) -> Mat6 {
let mut c = [[0.0f64; 6]; 6];
for i in 0..6 {
for j in 0..6 {
c[i][j] = a[i][j] - b[i][j];
}
}
c
}
/// 6×6 identity matrix.
fn mat6_identity() -> Mat6 {
let mut m = [[0.0f64; 6]; 6];
for i in 0..6 {
m[i][i] = 1.0;
}
m
}
/// Transpose of a 6×6 matrix.
fn mat6_transpose(a: &Mat6) -> Mat6 {
let mut t = [[0.0f64; 6]; 6];
for i in 0..6 {
for j in 0..6 {
t[j][i] = a[i][j];
}
}
t
}
/// Analytical inverse of a 3×3 matrix via cofactor expansion.
///
/// Returns `None` if |det| < 1e-12 (singular or near-singular).
fn mat3_inv(m: &Mat3) -> Option<Mat3> {
// Cofactors (signed minors)
let c00 = m[1][1] * m[2][2] - m[1][2] * m[2][1];
let c01 = -(m[1][0] * m[2][2] - m[1][2] * m[2][0]);
let c02 = m[1][0] * m[2][1] - m[1][1] * m[2][0];
let c10 = -(m[0][1] * m[2][2] - m[0][2] * m[2][1]);
let c11 = m[0][0] * m[2][2] - m[0][2] * m[2][0];
let c12 = -(m[0][0] * m[2][1] - m[0][1] * m[2][0]);
let c20 = m[0][1] * m[1][2] - m[0][2] * m[1][1];
let c21 = -(m[0][0] * m[1][2] - m[0][2] * m[1][0]);
let c22 = m[0][0] * m[1][1] - m[0][1] * m[1][0];
// det = first row · first column of cofactor matrix (cofactor expansion)
let det = m[0][0] * c00 + m[0][1] * c01 + m[0][2] * c02;
if det.abs() < 1e-12 {
return None;
}
let inv_det = 1.0 / det;
// M⁻¹ = (1/det) · Cᵀ (transpose of cofactor matrix)
Some([
[c00 * inv_det, c10 * inv_det, c20 * inv_det],
[c01 * inv_det, c11 * inv_det, c21 * inv_det],
[c02 * inv_det, c12 * inv_det, c22 * inv_det],
])
}
/// First 3 columns of a 6×6 matrix as a 6×3 matrix.
///
/// Because H = [I₃ | 0₃], P·Hᵀ equals the first 3 columns of P.
fn mat6x3_from_cols(p: &Mat6) -> [[f64; 3]; 6] {
let mut out = [[0.0f64; 3]; 6];
for i in 0..6 {
for j in 0..3 {
out[i][j] = p[i][j];
}
}
out
}
/// Top-left 3×3 sub-matrix of a 6×6 matrix.
///
/// Because H = [I₃ | 0₃], H·P·Hᵀ equals the top-left 3×3 of P.
fn mat3_from_top_left(p: &Mat6) -> Mat3 {
let mut out = [[0.0f64; 3]; 3];
for i in 0..3 {
for j in 0..3 {
out[i][j] = p[i][j];
}
}
out
}
/// Element-wise add of two 6-vectors.
fn vec6_add(a: Vec6, b: Vec6) -> Vec6 {
[
a[0] + b[0],
a[1] + b[1],
a[2] + b[2],
a[3] + b[3],
a[4] + b[4],
a[5] + b[5],
]
}
/// Multiply a 6×3 matrix by a 3-vector, yielding a 6-vector.
fn mat6x3_mul_vec3(m: &[[f64; 3]; 6], v: Vec3) -> Vec6 {
let mut out = [0.0f64; 6];
for i in 0..6 {
for j in 0..3 {
out[i] += m[i][j] * v[j];
}
}
out
}
/// Multiply a 3×3 matrix by a 3-vector, yielding a 3-vector.
fn mat3_mul_vec3(m: &Mat3, v: Vec3) -> Vec3 {
[
m[0][0] * v[0] + m[0][1] * v[1] + m[0][2] * v[2],
m[1][0] * v[0] + m[1][1] * v[1] + m[1][2] * v[2],
m[2][0] * v[0] + m[2][1] * v[1] + m[2][2] * v[2],
]
}
/// Element-wise subtract of two 3-vectors.
fn vec3_sub(a: Vec3, b: Vec3) -> Vec3 {
[a[0] - b[0], a[1] - b[1], a[2] - b[2]]
}
/// Multiply a 6×3 matrix by a 3×3 matrix, yielding a 6×3 matrix.
fn mat6x3_mul_mat3(a: &[[f64; 3]; 6], b: &Mat3) -> [[f64; 3]; 6] {
let mut out = [[0.0f64; 3]; 6];
for i in 0..6 {
for j in 0..3 {
for k in 0..3 {
out[i][j] += a[i][k] * b[k][j];
}
}
}
out
}
/// Build the discrete-time process-noise matrix Q.
///
/// Corresponds to piecewise-constant acceleration (white-noise acceleration)
/// integrated over a time step dt:
///
/// ```text
/// ┌ dt⁴/4·I₃ dt³/2·I₃ ┐
/// Q = σ² │ │
/// └ dt³/2·I₃ dt² ·I₃ ┘
/// ```
fn build_process_noise(dt: f64, q_a: f64) -> Mat6 {
let dt2 = dt * dt;
let dt3 = dt2 * dt;
let dt4 = dt3 * dt;
let qpp = dt4 / 4.0 * q_a; // position–position diagonal
let qpv = dt3 / 2.0 * q_a; // position–velocity cross term
let qvv = dt2 * q_a; // velocity–velocity diagonal
let mut q = [[0.0f64; 6]; 6];
for i in 0..3 {
q[i][i] = qpp;
q[i + 3][i + 3] = qvv;
q[i][i + 3] = qpv;
q[i + 3][i] = qpv;
}
q
}
// ---------------------------------------------------------------------------
// Tests
// ---------------------------------------------------------------------------
#[cfg(test)]
mod tests {
use super::*;
/// A stationary filter (velocity = 0) should not move after a predict step.
#[test]
fn test_kalman_stationary() {
let initial = [1.0, 2.0, 3.0];
let mut state = KalmanState::new(initial, 0.01, 1.0);
// No update — initial velocity is zero, so position should barely move.
state.predict(0.5);
let pos = state.position();
assert!(
(pos[0] - 1.0).abs() < 0.01,
"px should remain near 1.0, got {}",
pos[0]
);
assert!(
(pos[1] - 2.0).abs() < 0.01,
"py should remain near 2.0, got {}",
pos[1]
);
assert!(
(pos[2] - 3.0).abs() < 0.01,
"pz should remain near 3.0, got {}",
pos[2]
);
}
/// With repeated predict + update cycles toward [5, 0, 0], the filter
/// should converge so that px is within 2.0 of the target after 10 steps.
#[test]
fn test_kalman_update_converges() {
let mut state = KalmanState::new([0.0, 0.0, 0.0], 1.0, 1.0);
let target = [5.0, 0.0, 0.0];
for _ in 0..10 {
state.predict(0.5);
state.update(target);
}
let pos = state.position();
assert!(
(pos[0] - 5.0).abs() < 2.0,
"px should converge toward 5.0, got {}",
pos[0]
);
}
/// An observation equal to the current position estimate should give a
/// very small Mahalanobis distance.
#[test]
fn test_mahalanobis_close_observation() {
let state = KalmanState::new([3.0, 4.0, 5.0], 0.1, 0.5);
let obs = state.position(); // observation = current estimate
let d2 = state.mahalanobis_distance_sq(obs);
assert!(
d2 < 1.0,
"Mahalanobis distance² for the current position should be < 1.0, got {}",
d2
);
}
/// An observation 100 m from the current position should yield a large
/// Mahalanobis distance (far outside the uncertainty ellipsoid).
#[test]
fn test_mahalanobis_far_observation() {
// Use small obs_noise_var so the uncertainty ellipsoid is tight.
let state = KalmanState::new([0.0, 0.0, 0.0], 0.01, 0.01);
let far_obs = [100.0, 0.0, 0.0];
let d2 = state.mahalanobis_distance_sq(far_obs);
assert!(
d2 > 9.0,
"Mahalanobis distance² for a 100 m observation should be >> 9, got {}",
d2
);
}
}

297
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//! Track lifecycle state machine for survivor tracking.
//!
//! Manages the lifecycle of a tracked survivor:
//! Tentative → Active → Lost → Terminated (or Rescued)
/// Configuration for SurvivorTracker behaviour.
#[derive(Debug, Clone)]
pub struct TrackerConfig {
/// Consecutive hits required to promote Tentative → Active (default: 2)
pub birth_hits_required: u32,
/// Consecutive misses to transition Active → Lost (default: 3)
pub max_active_misses: u32,
/// Seconds a Lost track is eligible for re-identification (default: 30.0)
pub max_lost_age_secs: f64,
/// Fingerprint distance threshold for re-identification (default: 0.35)
pub reid_threshold: f32,
/// Mahalanobis distance² gate for data association (default: 9.0 = 3σ in 3D)
pub gate_mahalanobis_sq: f64,
/// Kalman measurement noise variance σ²_obs in m² (default: 2.25 = 1.5m²)
pub obs_noise_var: f64,
/// Kalman process noise variance σ²_a in (m/s²)² (default: 0.01)
pub process_noise_var: f64,
}
impl Default for TrackerConfig {
fn default() -> Self {
Self {
birth_hits_required: 2,
max_active_misses: 3,
max_lost_age_secs: 30.0,
reid_threshold: 0.35,
gate_mahalanobis_sq: 9.0,
obs_noise_var: 2.25,
process_noise_var: 0.01,
}
}
}
/// Current lifecycle state of a tracked survivor.
#[derive(Debug, Clone, PartialEq)]
pub enum TrackState {
/// Newly detected; awaiting confirmation hits.
Tentative {
/// Number of consecutive matched observations received.
hits: u32,
},
/// Confirmed active track; receiving regular observations.
Active,
/// Signal lost; Kalman predicts position; re-ID window open.
Lost {
/// Consecutive frames missed since going Lost.
miss_count: u32,
/// Instant when the track entered Lost state.
lost_since: std::time::Instant,
},
/// Re-ID window expired or explicitly terminated. Cannot recover.
Terminated,
/// Operator confirmed rescue. Terminal state.
Rescued,
}
/// Controls lifecycle transitions for a single track.
pub struct TrackLifecycle {
state: TrackState,
birth_hits_required: u32,
max_active_misses: u32,
max_lost_age_secs: f64,
/// Consecutive misses while Active (resets on hit).
active_miss_count: u32,
}
impl TrackLifecycle {
/// Create a new lifecycle starting in Tentative { hits: 0 }.
pub fn new(config: &TrackerConfig) -> Self {
Self {
state: TrackState::Tentative { hits: 0 },
birth_hits_required: config.birth_hits_required,
max_active_misses: config.max_active_misses,
max_lost_age_secs: config.max_lost_age_secs,
active_miss_count: 0,
}
}
/// Register a matched observation this frame.
///
/// - Tentative: increment hits; if hits >= birth_hits_required → Active
/// - Active: reset active_miss_count
/// - Lost: transition back to Active, reset miss_count
pub fn hit(&mut self) {
match &self.state {
TrackState::Tentative { hits } => {
let new_hits = hits + 1;
if new_hits >= self.birth_hits_required {
self.state = TrackState::Active;
self.active_miss_count = 0;
} else {
self.state = TrackState::Tentative { hits: new_hits };
}
}
TrackState::Active => {
self.active_miss_count = 0;
}
TrackState::Lost { .. } => {
self.state = TrackState::Active;
self.active_miss_count = 0;
}
// Terminal states: no transition
TrackState::Terminated | TrackState::Rescued => {}
}
}
/// Register a frame with no matching observation.
///
/// - Tentative: → Terminated immediately (not enough evidence)
/// - Active: increment active_miss_count; if >= max_active_misses → Lost
/// - Lost: increment miss_count
pub fn miss(&mut self) {
match &self.state {
TrackState::Tentative { .. } => {
self.state = TrackState::Terminated;
}
TrackState::Active => {
self.active_miss_count += 1;
if self.active_miss_count >= self.max_active_misses {
self.state = TrackState::Lost {
miss_count: 0,
lost_since: std::time::Instant::now(),
};
}
}
TrackState::Lost { miss_count, lost_since } => {
let new_count = miss_count + 1;
let since = *lost_since;
self.state = TrackState::Lost {
miss_count: new_count,
lost_since: since,
};
}
// Terminal states: no transition
TrackState::Terminated | TrackState::Rescued => {}
}
}
/// Operator marks survivor as rescued.
pub fn rescue(&mut self) {
self.state = TrackState::Rescued;
}
/// Called each tick to check if Lost track has expired.
pub fn check_lost_expiry(&mut self, now: std::time::Instant, max_lost_age_secs: f64) {
if let TrackState::Lost { lost_since, .. } = &self.state {
let elapsed = now.duration_since(*lost_since).as_secs_f64();
if elapsed > max_lost_age_secs {
self.state = TrackState::Terminated;
}
}
}
/// Get the current state.
pub fn state(&self) -> &TrackState {
&self.state
}
/// True if track is Active or Tentative (should keep in active pool).
pub fn is_active_or_tentative(&self) -> bool {
matches!(self.state, TrackState::Active | TrackState::Tentative { .. })
}
/// True if track is in Lost state.
pub fn is_lost(&self) -> bool {
matches!(self.state, TrackState::Lost { .. })
}
/// True if track is Terminated or Rescued (remove from pool eventually).
pub fn is_terminal(&self) -> bool {
matches!(self.state, TrackState::Terminated | TrackState::Rescued)
}
/// True if a Lost track is still within re-ID window.
pub fn can_reidentify(&self, now: std::time::Instant, max_lost_age_secs: f64) -> bool {
if let TrackState::Lost { lost_since, .. } = &self.state {
let elapsed = now.duration_since(*lost_since).as_secs_f64();
elapsed <= max_lost_age_secs
} else {
false
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use std::time::{Duration, Instant};
fn default_lifecycle() -> TrackLifecycle {
TrackLifecycle::new(&TrackerConfig::default())
}
#[test]
fn test_tentative_confirmation() {
// Default config: birth_hits_required = 2
let mut lc = default_lifecycle();
assert!(matches!(lc.state(), TrackState::Tentative { hits: 0 }));
lc.hit();
assert!(matches!(lc.state(), TrackState::Tentative { hits: 1 }));
lc.hit();
// 2 hits → Active
assert!(matches!(lc.state(), TrackState::Active));
assert!(lc.is_active_or_tentative());
assert!(!lc.is_lost());
assert!(!lc.is_terminal());
}
#[test]
fn test_tentative_miss_terminates() {
let mut lc = default_lifecycle();
assert!(matches!(lc.state(), TrackState::Tentative { .. }));
// 1 miss while Tentative → Terminated
lc.miss();
assert!(matches!(lc.state(), TrackState::Terminated));
assert!(lc.is_terminal());
assert!(!lc.is_active_or_tentative());
}
#[test]
fn test_active_to_lost() {
let mut lc = default_lifecycle();
// Confirm the track first
lc.hit();
lc.hit();
assert!(matches!(lc.state(), TrackState::Active));
// Default: max_active_misses = 3
lc.miss();
assert!(matches!(lc.state(), TrackState::Active));
lc.miss();
assert!(matches!(lc.state(), TrackState::Active));
lc.miss();
// 3 misses → Lost
assert!(lc.is_lost());
assert!(!lc.is_active_or_tentative());
}
#[test]
fn test_lost_to_active_via_hit() {
let mut lc = default_lifecycle();
lc.hit();
lc.hit();
// Drive to Lost
lc.miss();
lc.miss();
lc.miss();
assert!(lc.is_lost());
// Hit while Lost → Active
lc.hit();
assert!(matches!(lc.state(), TrackState::Active));
assert!(lc.is_active_or_tentative());
}
#[test]
fn test_lost_expiry() {
let mut lc = default_lifecycle();
lc.hit();
lc.hit();
lc.miss();
lc.miss();
lc.miss();
assert!(lc.is_lost());
// Simulate expiry: use an Instant far in the past for lost_since
// by calling check_lost_expiry with a "now" that is 31 seconds ahead
// We need to get the lost_since from the state and fake expiry.
// Since Instant is opaque, we call check_lost_expiry with a now
// that is at least max_lost_age_secs after lost_since.
// We achieve this by sleeping briefly then using a future-shifted now.
let future_now = Instant::now() + Duration::from_secs(31);
lc.check_lost_expiry(future_now, 30.0);
assert!(matches!(lc.state(), TrackState::Terminated));
assert!(lc.is_terminal());
}
#[test]
fn test_rescue() {
let mut lc = default_lifecycle();
lc.hit();
lc.hit();
assert!(matches!(lc.state(), TrackState::Active));
lc.rescue();
assert!(matches!(lc.state(), TrackState::Rescued));
assert!(lc.is_terminal());
}
}

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//! Survivor track lifecycle management for the MAT crate.
//!
//! Implements three collaborating components:
//!
//! - **[`KalmanState`]** — constant-velocity 3-D position filter
//! - **[`CsiFingerprint`]** — biometric re-identification across signal gaps
//! - **[`TrackLifecycle`]** — state machine (Tentative→Active→Lost→Terminated)
//! - **[`SurvivorTracker`]** — aggregate root orchestrating all three
//!
//! # Example
//!
//! ```rust,no_run
//! use wifi_densepose_mat::tracking::{SurvivorTracker, TrackerConfig, DetectionObservation};
//!
//! let mut tracker = SurvivorTracker::with_defaults();
//! let observations = vec![]; // DetectionObservation instances from sensing pipeline
//! let result = tracker.update(observations, 0.5); // dt = 0.5s (2 Hz)
//! println!("Active survivors: {}", tracker.active_count());
//! ```
pub mod kalman;
pub mod fingerprint;
pub mod lifecycle;
pub mod tracker;
pub use kalman::KalmanState;
pub use fingerprint::CsiFingerprint;
pub use lifecycle::{TrackState, TrackLifecycle, TrackerConfig};
pub use tracker::{
TrackId, TrackedSurvivor, SurvivorTracker,
DetectionObservation, AssociationResult,
};