MIMO Through-the-Wall Radar Micro-Doppler Signature Augmentation Method Based on Multi-Channel Information Fusion

Through-the-wall radar (TWR) can monitor and analyze the motion characteristics and activity patterns of indoor human targets, with the advantages of non-contact, high flexibility and privacy protection. However, existing TWR human activity recognition (HAR) techniques developed based on single-channel radar contain limited Doppler information, making it difficult to achieve accurate recognition on data where the direction of human motion is not parallel to the radar observation. To solve this problem, in this letter, a multi-input-multi-output (MIMO) TWR micro-Doppler signature augmentation method based on multi-channel information fusion is proposed. First, a multi-channel Doppler profile feature fusion method based on multi-scale wavelets with low-rank decomposition is presented. Then, a motion parameter estimation method based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) global optimization is proposed, and the fused Doppler profile transformation is implemented using the obtained orientation of human motion. Numerical simulated and measured experiments demonstrate the effectiveness of the proposed method.