Recursive spatial-temporal clustering-based target detection with millimeter-wave radar point cloud

Millimeter-wave (mmWave) radar plays a vital role in a wide range of applications such as security surveillance and environmental monitoring. This work investigates target detection with radar point cloud measurements in the slow-motion scenario. In contrast to the existing spatial domain clustering-based target detection methods, we adopt a recursive spatial-temporal clustering (STC)-based method to detect targets in the spatial and temporal domain jointly. Specifically, the points belonging to targets are obtained by clustering with a distance metric defined in the spatial-temporal domains. In addition, to ensure the feasibility of the proposed method for practical real-time implementation, a speed-up scheme that intends to reduce the computational complexity induced by clustering in both spatial and temporal dimensions is developed. We demonstrate the efficacy of the proposed recursive STC-based method through experimental mmWave radar point cloud data where multiple people walk simultaneously in an open space. The proposed method achieves decent target detection performance improvement compared to a widely-used clustering method for target detection while its computation time is negligible compared to radar data reception time.