Short-Time State-Space Method for Micro-Doppler Identification of Walking Subject Using UWB Impulse Doppler Radar

In this paper, ultra-wideband (UWB) Doppler radar signatures from walking human subjects are processed with the state-space method (SSM) to analyze compressed radar echoes for the first time. A new mathematical model is proposed to detect the human walking strides using UWB radar, while microDoppler (μ-D) features are extracted for gait analysis using shorttime SSM (STSSM). To distinguish μ-D signatures of different subjects' body parts, the SSM used for the characterization of radar target signatures and sensor fusion is applied on a sliding short-time window to enhance the resolution of feature extraction from data collected on a dismount. This method of the application of SSM to sliding short-time data is validated with a full-wave electromagnetic (EM) simulation of a walking subject using the Boulic model that represents the human kinematics. An EM scattering model is then utilized to compare the performance of a short-time Fourier transform with STSSM. Experimental results show that STSSM can be successfully applied to identify multiple μ-D trajectories in real experimental data, thus demonstrating the capability to positively identify human motions (right foot, left foot, and torso) even in a low signal-to-noise ratio environment.