基于涡旋电磁波雷达的人体目标步态精细识别

基于雷达的人体目标识别技术在诸多领域有着重要的应用。涡旋电磁波中携带有轨道角动量，在运动目标回波中会引起线多普勒和角多普勒效应，可为识别提供更丰富的信息。但与线多普勒相比，角多普勒较微弱，二者难以被分离。使用多发多收模型产生线性调频涡旋电磁波，通过双模态回波干涉抑制线多普勒。利用短时傅里叶变换，获得双模态回波的线多普勒和角多普勒时频图，将时频图的幅度值输入到双通道卷积神经网络模型中，获得分类结果。仿真实验结果表明，将线多普勒和角多普勒分离，同时输入到分类模型中可以提升人体步态精细识别能力。

Fine Recognition of Human Gait with Vortex Electromagnetic Wave Radar

Radar-based human target recognition technology has important applications in many fields. The vortex electromagnetic (EM) wave carries orbital angular momentum，and the target motion may cause the linear and angular Doppler effects. Vortex EM wave contains more target information，which can provide more information for identification. But the angular Doppler effect is much smaller than the linear Doppler effect，so it is difficult to separate them.A multi-transmitter and multi-receiver model is used to generate linear frequency modulation vortex electromagnetic wave，and the linear Doppler is suppressed by dual-mode echo interference. Then，the linear and angular Doppler time-frequency images of the dual-mode echo are obtained by using the short-time Fourier transform. The amplitude values of the time-frequency images are input into the dual-channel convolutional neural network model to obtain the classification results.The simulated results show that the fine recognition ability of human gait is improved by separating the linear Doppler and angular Doppler.