Doppler-Domain Warping and Resampling for Semi-Static Human Occupancy Detection Using Low-Resolution SIMO FMCW Radar

Radar-based human activity recognition is a promising privacy-preserving alternative to vision and wearable sensors, especially in sensitive environments such as long-term care facilities. However, reliable detection of quasi-static human presence, where subtle micro-motions such as small postural sway, breathing, small limb readjustments, etc, concentrate their energy around near-zero Doppler using low-resolution radar, remains challenging. In this work, we propose Range-Angle based Semi-Static Occupancy (RASSO), an invertible Doppler-domain non-linear remapping layer that resamples the slow-time FFT (Doppler) axis to densify the sampling grid around near-zero Doppler before adaptive Capon beamforming. This produces Range-Azimuth (RA) feature maps with higher Signal-to-Noise ratios (SNR), resulting in sharpened target peaks and reduced background variance. On a real dataset collected in a nursing home using short-range SIMO FMCW radar, RASSO consistently yields higher-contrast RA maps that are easier to threshold and more informative for downstream models. Under cell-averaging constant false alarm rate (CA-CFAR), RASSO-enhanced RA (RASSO-RA) achieves the highest detection-curve area under the curve (AUC = 0.981) and the best recall at false alarm rates (FAR) of 1% and 5% (0.920 and 0.947), compared to conventional Capon beam formation and a recent state-of-the-art baseline. Training on RASSO-RA further improves data-driven methods: a frame-based SimpleCNN reaches 95–99% accuracy across subjects, while a sequence-based CNN–LSTM achieves 99.4–99.6% accuracy, with paired session-level bootstrap analysis confirming statistically significant macro-F1 improvements of 2.6–3.6 percentage points (95% confidence intervals strictly above zero) over the non-warped baseline.