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基于WiFi信道状态信息的人员活动持续时间估计
引用本文:刘立双,魏忠诚,张春华,王巍,赵继军.基于WiFi信道状态信息的人员活动持续时间估计[J].计算机应用,2019,39(7):2056-2060.
作者姓名:刘立双  魏忠诚  张春华  王巍  赵继军
作者单位:河北工程大学信息与电气工程学院,河北邯郸056038;河北省安防信息感知与处理重点实验室(河北工程大学),河北邯郸056038;河北工程大学公共体育部,河北邯郸,056038
基金项目:国家自然科学基金资助项目(61802107);河北省自然科学基金资助项目(F2018402251);邯郸市科学技术研究与发展计划项目(1721203048);河北省物联网数据采集与处理工程技术研究中心开放课题(2016-2)。
摘    要:针对传统人员活动持续时间估计系统隐私性及灵活性较差的问题,分析信道状态信息(CSI)的幅度变化,提出了一个基于WiFi信道状态信息的人员活动持续时间估计系统。该系统重点将连续复杂的人员活动持续时间估计问题转化为离散简单的人员检测问题。首先,采集CSI信息并滤除异常值和噪声;其次,利用主成分分析法(PCA)进行子载波降维,获取主成分以及相应的特征向量;随后计算主成分方差和特征向量一阶差分均值,并将两者比值作为特征值训练反向传输神经网络(BPNN)模型;然后,利用训练好的BPNN模型进行人员检测,并当检测出有人员活动时,进一步对CSI数据进行等宽分割;最后,针对所有分割后的CSI数据实现人员检测,并依据符合人员检测结果的数据来估计人员活动的持续时间。在真实室内环境中对系统进行实验评估,人员检测平均准确率可达到97%,活动持续时间误差不超过10%。实验结果表明,该系统能够有效估计出人员活动的持续时间。

关 键 词:人员活动持续时间  信道状态信息  反向传输神经网络  人员活动检测  WIFI
收稿时间:2018-12-10
修稿时间:2019-01-29

Lifetime estimation for human motion with WiFi channel state information
LIU Lishuang,WEI Zhongcheng,ZHANG Chunhua,WANG Wei,ZHAO Jijun.Lifetime estimation for human motion with WiFi channel state information[J].journal of Computer Applications,2019,39(7):2056-2060.
Authors:LIU Lishuang  WEI Zhongcheng  ZHANG Chunhua  WANG Wei  ZHAO Jijun
Affiliation:1. School of Information and Electrical Engineering, Hebei University of Engineering, Handan Hebei 056038, China;
2. Hebei Key Laboratory of Security and Protection Information Sensing and Processing(Hebei University of Engineering), Handan Hebei 056038, China;
3. Department of Public Sports, Hebei University of Engineering, Handan Hebei 056038, China
Abstract:Concerning the poor privacy and flexibility of traditional lifetime estimation for human motion, a lifetime estimation system for human motion was proposed, by analyzing the amplitude variation of WiFi Channel State Information (CSI). In this system, the continuous and complex lifetime estimation problem was transformed into a discrete and simple human motion detection problem. Firstly, the CSI was collected with filtering out the outliers and noise. Secondly, Principal Component Analysis (PCA) was used to reduce the dimension of subcarriers, obtaining the principal components and the corresponding eigenvectors. Thirdly, the variance of principal components and the mean of first difference of eigenvectors were calculated, and a Back Propagation Neural Network (BPNN) model was trained with the ratio of above two parameters as eigenvalue. Fourthly, human motion detection was achieved by the trained BP neural network model, and the CSI data were divided into some segments with equal width when the human motion was detected. Finally, after the human motion detection being performed on all CSI segments, the human motion lifetime was estimated according to the number of CSI segments with human motion detected. In real indoor environment, the average accuracy of human motion detection can reach 97% and the error rate of human motion lifetime is less than 10%. The experimental results show that the proposed system can effectively estimate the lifetime of human motion.
Keywords:human motion lifetime                                                                                                                        Channel State Information (CSI)                                                                                                                        Back Propagation Neural Network (BPNN)                                                                                                                        human motion detection                                                                                                                        WiFi
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