Adaptive Micro-Doppler Corner Feature Extraction Method Based on Difference of Gaussian Filter and Deformable Convolution

Through-the-wall radar (TWR) utilizes range and Doppler information to achieve indoor human activity recognition. However, traditional recognition methods are developed based on range-time maps (RTM) and Doppler-time maps (DTM), resulting in low accuracy and poor robustness. In order to solve these problems, this letter proposes to use micro-Doppler corner feature to achieve activity recognition and gives an adaptive corner feature extraction method based on difference of Gaussian (DoG) filter and deformable convolution. Micro-Doppler corner feature is defined as the points on the radar squared-range and squared-Doppler images where the gray scale changes sharply in different directions, reflecting the inflection, stationing, intersection, and boundaries of the motion trajectory curves of the human limb nodes. The proposed corner feature extraction method utilizes the DoG filter to extract the micro-Doppler corner supervisory labels on simulated data. The labels are then used to train the μD-CornerDet, which is constructed based on deformable convolution network (DCN), task-adaptive deformable convolution network (TDCN), feature pyramid network (FPN) and learnable regression global attention module (LRGA). For predictions, only μD-CornerDet is used on measured data to obatin the corner feature maps. Both numerical simulations and experiments are conducted to verify the effectiveness and robustness of the proposed method.