Efficient SVD Techniques to Overcome Interference and Obstacle Challenges for Micro-Doppler Extraction in FMCW Radars

This article investigates the extraction of micro-Doppler (MD) signatures in frequency-modulated continuous-wave (FMCW) radar data under challenging scenarios, such as detecting hidden pedestrian targets, while the radar is operating in the presence of interference from other FMCW radars. Reliable detection of MD signatures is essential for applications such as pedestrian recognition in autonomous vehicle systems. However, challenges arise due to interference from other FMCW radars and reflections caused by environmental obstructions, such as parked vehicles and cars ahead on the road. To address this, we first show that the short-time Fourier transform (STFT), which is commonly used for MD extraction, is not sufficient under challenging scenarios. We show that the singular value decomposition (SVD) of a matrix of in-phase (I) and quadrature (Q) complex radar samples can be used as a preprocessing step for MD extraction under challenging scenarios. However, for MD extraction, SVD needs to be performed on a large IQ sample matrix, which can be computationally intensive for radar data processing system on chips (SoCs). We study various computationally efficient SVD methods, such as incremental SVD (ISVD) and randomized SVD (RSVD). We applied ISVD and RSVD to separate pedestrian movements from other targets and radar interference signals. The effectiveness of various SVD techniques was evaluated based on their ability to preserve MD features while minimizing processing time. Results indicate that ISVD and RSVD successfully extract pedestrian MD characteristics despite mutual radar interference and signal reflections from other targets acting as obstacles. Additionally, computational efficiency and reconstruction error were compared for the different SVD methods, revealing tradeoffs between the various approaches. Our findings suggest that both RSVD and ISVD methods offer promising solutions for FMCW-radar-based MD extraction analysis under challenging scenarios.