fca5e6f0a0
#1170 — live multistatic bridge fed raw, un-canonicalized per-node CSI (64/128/192 bins) to MultistaticFuser, tripping DimensionMismatch every cycle and silently disabling fusion on mixed HT20/HT40 meshes. Add HardwareNormalizer::resample_to_canonical (resample-only, no z-score) and canonicalize every node frame onto the 56-tone grid before fusion. #1180 — update_csi_fps_ema only rejected dt<=0 or >=1s, so sub-ms UDP-burst arrivals (36us -> ~27kHz) inflated csi_fps_ema 40-840x. Add a 5ms plausibility floor and stop re-anchoring observe_csi_frame_arrival on burst deltas. #1183 — global ENOMEM backoff (CSI flood) starved <=48B/<=1Hz control packets. Add stream_sender_send_priority() bypassing the backoff gate without touching the streak; route feature_state/HEALTH/sync through it. Fix the misleading "HEALTH sent" log that printed even on rv_mesh_send failure. Verified: signal 501, sensing-server 677 tests (0 failed); firmware builds clean. Claude-Session: https://claude.ai/code/session_01AgpTcBLRJ32hUsKWxDXf36 |
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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