Multi-Target Separation Algorithm Based on Range-Doppler Map

To enhance the application of deep learning networks in recognizing multi-target human actions, this paper prposes an innovative multi-target separation algorithm utilizing Range-Doppler Maps (RDMs). The algorithm translates Frequency Modulated Continuous Wave (FMCW) radar data into visual RDMs and employs a threshold-based method to distinguish targets across successive frames, with the frame count optimized according to the modal value. By projecting the RDM onto the velocity axis and aggregating column-wise data incrementally, a micro-Doppler Map (mDM) is constructed. Experiments verify the algorithm's effectiveness with minimal impact on recognition accuracy, highlighted by an average Intersection over Union (IoU) score of 92.53%.