基于散射分离的多通道雷达人体行为识别方法  

Human activity recognition method using multi-channel radar based on scattering separation

作　　者：李未一 杨健 方旖[2] 贾勇 张伟[4] LI Weiyi;YANG Jian;FANG Yi;JIA Yong;ZHANG Wei(School of Mechanical and Electrical Engineering,Chengdu University of Technology,Chengdu 610059,China;Laboratory of Electromagnetic Space Cognition and Intelligent Control,Beijing 100089,China;School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100089,China;School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]成都理工大学机电工程学院,成都610059 [2]电磁空间认知与智能控制技术实验室,北京100089 [3]北京理工大学网络空间安全学院,北京100089 [4]电子科技大学信息与通信工程学院,成都611731

出　　处：《电波科学学报》2025年第1期172-183,共12页Chinese Journal of Radio Science

基　　金：四川省科技厅计划项目(2022YFS0531);国家自然科学基金(U20B2070);衢州市政府资助项目(2022D008,2022D005)。

摘　　要：人体目标相对于雷达呈现典型的多散射特性,强散射的躯干部位回波会掩盖四肢和头部等弱散射部位回波,限制了行为识别性能。基于此,本文提出一种基于散射分离的多通道雷达人体行为识别方法。首先,将多个收发通道的人体回波数据堆叠后进行主成分分析,强散射躯干和弱散射四肢头部被分离到前两个分量中,避免了掩盖影响;然后分别进行短时傅里叶变换得到对应躯干和四肢头部运动的时频谱图,共同对人体行为进行特征表达;最后分别计算谱图的方向梯度直方图特征,拼接形成人体行为特征,输入支持向量机完成识别。利用2发4收步进变频雷达采集6种行为的数据集,测试结果表明,相比于未散射分离,该方法的平均识别率提升了4.26%,行为特征得到充分表达,为人体行为识别提供了新的思路。Human targets display typical properties of multiple scattering relative to radar,and the strong scattering echoes from the torso often conceal the weaker scattering echoes from the limbs,head,and other body parts,thereby limiting the performance of activity recognition.To address this issue,a human activity recognition method using multi-channel radar based on scattering separation is proposed;firstly,the echo data of humans from multiple transmit-receive channels is stacked,followed by principal component analysis to separate the strong scattering echoes from the torso and the weaker scattering echoes from the limbs,head,and others into the first two components,which helps in avoiding the interference caused by concealment effects.Subsequently,short-time Fourier transforms are conducted individually to obtain time-frequency spectrograms corresponding to trunk movements and limb-head movements,expressing the characteristics of human activities jointly;after that,the histograms of oriented gradients for the two spectrograms are calculated,and the two features are then combined to form human activity characteristics,which are inputted into a support vector machine for recognition.Datasets encompassing six activities are gathered using a two-transmit-four-receive stepped-frequency radar.Test results demonstrate that this proposed method enhances the average recognition rate by 4.26%compared to approaches without scattering separation,and features of activities are fully expressed,which provides a new idea for human behavior recognition.

关 键 词：散射分离 多通道雷达 人体行为识别 主成分分析(PCA) 支持向量机(SVM) 

分 类 号：TN95[电子电信—信号与信息处理]