872d7593bb
* fix(firmware): on_send ESP-NOW callback compat for IDF v6.0 (closes #944) ESP-IDF v6.0 changed `esp_now_send_cb_t` from void (*)(const uint8_t *mac, esp_now_send_status_t status) to void (*)(const esp_now_send_info_t *tx_info, esp_now_send_status_t status) The C6 sync ESP-NOW path's `on_recv` was already version-guarded with `#if ESP_IDF_VERSION >= ESP_IDF_VERSION_VAL(5, 0, 0)` (lines 102-112) but the `on_send` sibling missed the equivalent guard. CI runs against IDF v5.4 so the regression slipped through; the reporter on IDF v6.0.1 with xtensa-esp-elf esp-15.2.0_20251204 hit: c6_sync_espnow.c:182:30: error: passing argument 1 of 'esp_now_register_send_cb' from incompatible pointer type [-Wincompatible-pointer-types] Fix: mirror the recv guard with `#if ESP_IDF_VERSION_MAJOR >= 6` since the send-callback signature change happened at IDF v6.0 (not v5.x like the recv-callback). Both branches ignore the address-side argument since `on_send` only inspects `status` to bump the TX-fail counter. Adds `#include "esp_idf_version.h"` so the macro is in scope. Closes #944 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(signal): anchor estimate_occupancy noise floor to calibration (closes #942) `test_estimate_occupancy_noise_only` asserts that 20 noise-only frames fed through a 50-frame calibrated `FieldModel` yield 0 occupancy. Failure reported on the upstream Linux + BLAS build. Root cause Calibration and estimation each compute their own Marcenko-Pastur threshold: threshold = noise_var · (1 + sqrt(p / N))² with `noise_var` = median of the bottom half of positive eigenvalues from their own covariance. The MP ratio differs across the two phases: calibration (50 frames, p=8): ratio = 0.16, factor ≈ 1.96 estimation (20 frames, p=8): ratio = 0.40, factor ≈ 2.66 On a small estimation window the local `noise_var` estimate can also be smaller than the calibration's (fewer samples → bottom-half median hits lower-magnitude eigenvalues). The combination of a smaller noise_var on estimation and the larger MP factor can flip eigenvalues on/off the "significant" line in a sample-size-dependent way, so an identical-distribution test window scores `significant > baseline_eigenvalue_count` and reports phantom persons. Fix Persist the calibration `noise_var` on `FieldNormalMode` (new field `baseline_noise_var: f64`) and use `max(local_noise_var, baseline_noise_var)` as the noise floor inside `estimate_occupancy`. This anchors the threshold to the calibration scale and prevents the short-window collapse without changing behavior when the local window's own noise dominates (the real-motion case). `baseline_noise_var` defaults to 0.0 in the diagonal-fallback paths; the estimation code treats 0.0 as "no anchored floor available" and preserves the pre-#942 single-window behavior — so older `FieldNormalMode` instances deserialised from disk continue to work unchanged. Test results cargo test --workspace --no-default-features → 413 lib tests pass (signal crate), 0 fail, 1 ignored. The actual `eigenvalue`-gated test still requires BLAS (not buildable on Windows). Logic-trace via the four numerical anchors above shows the fix flips `noise_var` from the smaller local value back up to the calibration scale, dropping `significant` to or below `baseline_eigenvalue_count` so the saturating subtraction returns 0. Closes #942 Co-Authored-By: claude-flow <ruv@ruv.net> |
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README.md
wifi-densepose-signal
State-of-the-art WiFi CSI signal processing for human pose estimation.
Overview
wifi-densepose-signal implements six peer-reviewed signal processing algorithms that extract
human motion features from raw WiFi Channel State Information (CSI). Each algorithm is traced
back to its original publication and integrated with the
ruvector family of crates for high-performance
graph and attention operations.
Algorithms
| Algorithm | Module | Reference |
|---|---|---|
| Conjugate Multiplication | csi_ratio |
SpotFi, SIGCOMM 2015 |
| Hampel Filter | hampel |
WiGest, 2015 |
| Fresnel Zone Model | fresnel |
FarSense, MobiCom 2019 |
| CSI Spectrogram | spectrogram |
Common in WiFi sensing literature since 2018 |
| Subcarrier Selection | subcarrier_selection |
WiDance, MobiCom 2017 |
| Body Velocity Profile (BVP) | bvp |
Widar 3.0, MobiSys 2019 |
Features
- CSI preprocessing -- Noise removal, windowing, normalization via
CsiProcessor. - Phase sanitization -- Unwrapping, outlier removal, and smoothing via
PhaseSanitizer. - Feature extraction -- Amplitude, phase, correlation, Doppler, and PSD features.
- Motion detection -- Human presence detection with confidence scoring via
MotionDetector. - ruvector integration -- Graph min-cut (person matching), attention mechanisms (antenna and spatial attention), and sparse solvers (subcarrier interpolation).
Quick Start
use wifi_densepose_signal::{
CsiProcessor, CsiProcessorConfig,
PhaseSanitizer, PhaseSanitizerConfig,
MotionDetector,
};
// Configure and create a CSI processor
let config = CsiProcessorConfig::builder()
.sampling_rate(1000.0)
.window_size(256)
.overlap(0.5)
.noise_threshold(-30.0)
.build();
let processor = CsiProcessor::new(config);
Architecture
wifi-densepose-signal/src/
lib.rs -- Re-exports, SignalError, prelude
bvp.rs -- Body Velocity Profile (Widar 3.0)
csi_processor.rs -- Core preprocessing pipeline
csi_ratio.rs -- Conjugate multiplication (SpotFi)
features.rs -- Amplitude/phase/Doppler/PSD feature extraction
fresnel.rs -- Fresnel zone diffraction model
hampel.rs -- Hampel outlier filter
motion.rs -- Motion and human presence detection
phase_sanitizer.rs -- Phase unwrapping and sanitization
spectrogram.rs -- Time-frequency CSI spectrograms
subcarrier_selection.rs -- Variance-based subcarrier selection
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-core |
Foundation types and traits |
ruvector-mincut |
Graph min-cut for person matching |
ruvector-attn-mincut |
Attention-weighted min-cut |
ruvector-attention |
Spatial attention for CSI |
ruvector-solver |
Sparse interpolation solver |
License
MIT OR Apache-2.0