Wi-HFM:基于WiFi信道特征的人流量监测方法  

Wi-HFM: A Human Flow Monitoring Method Based on WiFi Channel Features

作　　者：杨志勇[1] 卢超 王俊杰 Yang Zhiyong;Lu Chao;Wang Junjie(School of Software,Nanchang Hangkong University,Nanchang 330063)

机构地区：[1]南昌航空大学软件学院,南昌330063

出　　处：《计算机研究与发展》2025年第3期720-732,共13页Journal of Computer Research and Development

基　　金：国家自然科学基金青年科学基金项目(61501218);江西省自然科学基金项目(20181BAB202015)。

摘　　要：随着人们对人数统计需求的不断增长,基于信道状态信息(channel state information,CSI)的人流量监测技术因其易于部署、保护隐私和适用性强等优势而备受关注.然而,在现有的人流量监测工作中,人数识别的准确率容易受到人群密集程度的影响.为了保证监测精度,通常只能在人群稀疏的情况下进行监测,这导致了基于CSI的人流量监测技术缺乏实用性.为了解决这一问题,提出了一种能够识别连续性人流的监测方法.该方法首先利用解卷绕和线性相位校正算法,对原始数据进行相位补偿并消除随机相位偏移;然后通过标准差和方差提取连续性人流数据中的有效数据包;最后将时域上的相位差信息作为特征信号输入到深度学习的CLDNN(convolutional,long short-term memory,deep neural network)中进行人数识别.经过实验测试,该方法在前后排行人距离不小于1 m的情况下,分别实现了室外96.7%和室内94.1%的准确率,优于现有的人流量监测方法.With the increasing demand for people counting,the technology of human flow monitoring based on channel state information(CSI)attracts much attention because of its advantages such as easy deployment,privacy protection and strong applicability.However,in the existing human flow monitoring work,the accuracy of pedestrian recognition is easily affected by the density of the crowd.To ensure the monitoring accuracy,the monitoring can only be carried out when the crowd is sparse,which leads to the lack of practicability of the human flow monitoring technology based on CSI.In order to solve this problem,a monitoring method that can identify continuous flow of people is proposed.The method firstly uses phase unwrapping and linear phase correction algorithm to eliminate random phase offset and phase compensation for original data,then extracts valid data packets from continuous flow data by standard deviation and variance,and finally inputs phase difference information in the time domain as feature signals into the deep learning convolutional,long short-term memory,deep neural network(CLDNN)for pedestrian recognition.After actual testing,the method achieves outdoor accuracy of 96.7%and indoor accuracy of 94.1%under the condition that the distance between pedestrians in front and back is not less than 1 m,outperforming the existing method of human flow monitoring.

关 键 词：信道状态信息 人流量监测 连续性人流 相位校正 深度学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]