基于FMCW雷达和ResNeSt-GRU的行为识别方法

Behavior Recognition Method Based on FMCW Radar and ResNeSt-GRU

针对FMCW雷达在行为识别方面的应用,提出一种基于分离注意力残差神经网络(ResNeSt)和门控神经单元(GRU)的人体行为识别系统。使用调频连续波(FMCW)雷达采集人体行为数据,之后采用快速傅里叶变换算法(FFT)提取雷达数据每一帧距离、速度和角度维信息,按照时间维度拼接成距离时间图(RTM)、多普勒时间图(DTM)和角度时间图(ATM),最后以RTM、DTM和ATM作为输入样本,采用三流ResNeSt-GRU模型对不同人体行为进行识别。实验结果表明,三流ResNeSt-GRU模型对8种行为的平均识别准确率达到了98.92%,均高于传统和融合式深度学习模型。此外,采用该模型比传统特征融合之后采用单流网络的识别准确率提高了2.3%。因而该系统可以有效提高人体行为识别系统的识别准确率,为人体行为识别提供新的技术方法。

Aiming at the application of frequency modulated continuous wave radar in behavior recognition,a human behavior recognition system based on split attention residual neural network(ResNeSt)and gated neural unit(GRU)is proposed.The frequency modulated continuous wave(FMCW)radar is used to collect human behavior data.The fast Fourier transform algorithm(FFT)is used to extract the distance,velocity and angle dimension information of each frame of radar data,and then stitch them according to the time dimension into Range-Time Map(RTM),Doppler-Time Map(DTM)and Angle-Time Map(ATM).Finally,RTM,DTM and ATM are used as input samples,and the three-stream ResNeSt-GRU model is used to recognize different human behaviors.The experimental results show that the average recognition accuracy of the three-stream ResNeSt-GRU model for 8 behaviors reaches 98.92%,which is higher than the traditional deep learning model and the fusion deep learning model.In addition,the recognition accuracy rate using this model is 2.3% higher than that using a single-stream network after traditional feature fusion.Therefore,the system can effectively improve the recognition accuracy of the human behavior recognition system,and provide a new technology for the human behavior recognition.