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.

在雷达观测下，每个运动目标由于其结构分量运动而包含独特的微多普勒（mD）特征，这被认为是其识别中的目标类别特征之一。在雷达观测通道中同时存在多个运动目标的情况下，识别目标类型及其运动参数分析变得具有挑战性。本文旨在借助基于机器学习的信号处理方法，将所需目标的微多普勒 (mD) 特征与在单通道雷达中捕获的多个移动目标的响应分离。回归深度神经网络旨在产生概率时频掩码，它将所需的目标时频特征与来自不同类别的更多目标的混合 mD 响应分开。与清洁条件下的微多普勒特征相比，所提出的方法提供了分离运动特征的高度相关性。