Multi-Person Recognition Using Separated Micro-Doppler Signatures

It is challenging to recognize individuals when they move in the radar field of view due to the superimposition of micro-Doppler signatures. This paper presents a multi-person recognition approach by separating micro-Doppler signatures of multiple persons into their individual components. The preliminary separation can be obtained by their range difference in a high resolution radar. A multi-task learning network is designed for both the fine separation of micro-Doppler signatures and the personnel recognition. A frequency modulated continuous waveform (FMCW) radar that operates at 77 GHz for automotive applications is used in experiments. The proposed deep-neural-network-based approach gives a convincing result in the test.