Analysis of micro-Doppler signatures for vital sign detection using UWB impulse Doppler radar

The joined range-time-frequency representation of ultra-wideband (UWB) Doppler radar signatures from a walking human subject is processed with a state space method (SSM) in which micro-Doppler (m-D) features are extracted for vital sign analysis. To clearly distinguish respiration rates from moving subjects, the SSM, originally developed for radar target identification and sensor fusion, is applied in a sliding short-time window for enhanced resolution in vital sign detection. This application of SSM to sliding short-time data, termed hereafter as short-time SSM (STSSM), is validated with a full-wave electromagnetic simulation of a walking subject using the Boulic model to represent the kinematics. The scattering model is utilized to calibrate the state space system parameters before it is applied to experimental UWB radar data. The cross correlation and weight functions are utilized to cancel the random motions attributed by walking from a human subject, prior to the application of STSSM to UWB signal. The results show that STSSM can be successfully utilized to accurately measure vital signs in real experimental data, thus demonstrating the capability to positively identify respiration rates even in a low signal-to-noise ratio environment.