Deep neural network-based target separation from mixed micro-Doppler signature of multiple moving targets

Under radar observation, every moving target contains a unique micro-Doppler (m-D) signature due to its structural component movement, which has been considered as one of the target class characteristics in their identification. It becomes challenging to identify the type of target and its motion parameter analysis for the simultaneous presence of multiple moving targets in the radar observation channel. This article aims to separate the desired target’s micro-Doppler (m-D) signature from the responses of multiple moving targets captured in a single channel radar with the help of a machine learning-based signal processing approach. A regressive deep neural network is designed to produce the probabilistic time-frequency mask, which separates the desired target time-frequency signature from the mixed m-D responses of more targets from different classes. The proposed method provides a high correlation of separated movement signature in comparison to the clean condition micro-Doppler signatures.