基于并行空时深度学习网络的无设备身份识别方法

DEVICE-FREE IDENTIFICATION METHOD BASED ON PARALLEL SPACE-TIME DEEP LEARNING NETWORK

针对现有人员身份识别方法存在灵活性差、特征提取不足的问题,分析人体步态对信道状态信息(CSI)幅值产生的影响,提出一种基于Wi-Fi信道状态信息的无设备人员身份识别方法。将CSI幅值矩阵的每列子载波作为样本,利用小波变换获取样本中代表人员活动的低频信息。通过并行深度学习模型WiD(Wi-Fi identification)提取样本中人体步态的大小和发生的先后顺序,合并两种步态特征作为人员的身份特征。通过Softmax函数进行分类,实现身份识别。与当前先进方法相比,在未明显增加时间代价和空间代价的条件下,该方法获得了更高的身份识别精度和更好的模型鲁棒性。

Aiming at the problems of poor flexibility and inadequate feature extraction in the existing methods of personal identification,the influence of human gait on the amplitude of channel state information(CSI)is analyzed,and a device-free person identification based on Wi-Fi channel state information is proposed.It took each column of subcarriers of the CSI amplitude matrix as a sample,and used wavelet transform to obtain low-frequency information representing the activities of people in the sample.We used parallel deep learning models WiD(Wi-Fi identification)to extract the size and sequence of human gait in the sample,and merged the two gait characteristics as the identity characteristics of the person.We classified them through the Softmax function to achieve identity recognition.Compared with current advanced methods,this method achieves higher identification accuracy and better model robustness without significantly increasing time and space costs.