Multi-person micro-Doppler signal decomposition based on millimeter wave radar

Aiming at the problem of spectrum aliasing of micro-Doppler signature generated by millimeter-wave radar when many people move at the same time, a multi-person micro-Doppler signal decomposition method based on millimeter-wave radar is proposed. By preprocessing the data collected by radar, the position information is obtained by using the time-distance map, and the range of single person's position is divided by using the range bin according to the position information, and the micro-Doppler signature is obtained by short-time Fourier transform of different range bins to extract human posture characteristics. The micro-Doppler signature obtained by decomposing micro-Doppler signals are used to extract different gesture features when multiple people act at the same time. The separated micro-Doppler signatures are recognized in VGG16, ResNet50, DenseNet121 and MobileNetV2 convolutional neural networks, and the recognition accuracy is as high as 99%, 98%, 99.5% and 98.5% respectively. Experiments verify the effectiveness of the multi-person micro-Doppler signal decomposition method based on millimeter-wave radar.