Radar micro-doppler signature analysis and its application on gait recognition

Micro-Doppler signature (mDS) was often utilized for radar target recognition in literature. Most existing approaches focus on extracting visual features for human operators. In this paper, we provide a complete solution to gait recognition using radar micro-Doppler analysis. Gait recognition is challenging due to the time-varying nature of micro-Doppler signature and the small differences of human gaits among different people. To align two mDSs in time, we propose to utilize dynamic time warping (DTW). To uncover the tiny differences among people, we propose to treat the distances of a sample to all gallery samples as the feature vector, and classify it using a support vector machine. To evaluate the performance of the proposed approach, we create an mDS-gait database. On this database, the proposed approach demonstrates superior performance compared with existing ones.