A Feature-based Filtering Algorithm with 60GHz MIMO FMCW Radar for Indoor Detection and Trajectory Tracking

A feature-based filtering algorithm is proposed to solve the difficult problem of indoor human body positioning. The algorithm is based on multiple-input multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar, including noise floor removal in the time domain, adaptive mean cancellation in the frequency domain, and non-coherent accumulation for all virtual array elements. As for different postures of the human body, such as standing posture, sitting posture, lying posture, movement, etc., the raw radar data is processed to remove noise and to obtain an enhanced human target signal. Then the 4D point clouds of the target is obtained through the TLS-ESPRIT method and digital beamforming (DBF). The experimental results show that the proposed algorithm has a strong enhancement effect on human targets, and the effective targets’ signal to noise ratio has increased by more than 90dB.