通信学报 ›› 2019, Vol. 40 ›› Issue (4): 160-170.doi: 10.11959/j.issn.1000-436x.2019082

• 学术论文 • 上一篇    下一篇

基于信道状态信息的无源室内人员日常行为检测方法

党小超1,2,黄亚宁1,郝占军1,2(),司雄1   

  1. 1 西北师范大学计算机科学与工程学院,甘肃 兰州 730070
    2 甘肃省物联网工程研究中心,甘肃 兰州 730070
  • 修回日期:2019-02-04 出版日期:2019-04-25 发布日期:2019-05-05
  • 作者简介:党小超(1963- ),男,陕西韩城人,西北师范大学教授、硕士生导师,主要研究方向为物联网、传感器网络、无线感知技术等。|黄亚宁(1994- ),女,甘肃徽县人,西北师范大学硕士生,主要研究方向为无线定位技术、室内人体感知技术。|郝占军(1979- ),男,河北邢台人,西北师范大学副教授、硕士生导师,主要研究方向为位置服务、无线定位技术。|司雄(1993- ),男,甘肃白银人,西北师范大学硕士生,主要研究方向为位置服务、无线定位技术。
  • 基金资助:
    国家自然科学基金资助项目(61762079);国家自然科学基金资助项目(61662070);甘肃省科技重点研发基金资助项目(1604FKCA097);甘肃省科技重点研发基金资助项目(17YF1GA015)

Passive indoor human daily behavior detection method based on channel state information

Xiaochao DANG1,2,Yaning HUANG1,Zhanjun HAO1,2(),Xiong SI1   

  1. 1 College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China
    2 Gansu Province Internet of Things Engineering Research Center,Lanzhou 730070,China
  • Revised:2019-02-04 Online:2019-04-25 Published:2019-05-05
  • Supported by:
    The National Natural Science Foundation of China(61762079);The National Natural Science Foundation of China(61662070);The Key Science and Technology Development Program of Gansu Province(1604FKCA097);The Key Science and Technology Development Program of Gansu Province(17YF1GA015)

摘要:

利用CSI的室内人员日常行为检测在无线传感网领域如火如荼地发展着,但大多数研究仍停留在2.4 GHz的环境下,因此在检测率、顽健性、整体性能等方面还亟待提高。为解决此类问题,提出了一种基于CSI信号的无源室内人员日常行为检测方法HDFi,该方法在5 GHz的环境下对室内人员日常行为检测进行进一步的研究。所提检测方法分为3步:数据采集、数据处理、特征提取与在线检测。首先,实验在环境复杂的实验室及相对空旷的会议室采集典型的日常行为动作的数据;然后,提取特征较为明显的振幅和相位数据,使用低通滤波对信号特征进行处理,得到一组稳定及无噪声干扰的数据;最后,有效建立指纹库,进行在线检测,利用 SVM 算法对采集到的数据特征进行分类,提取较为稳定的特征值,建立一个室内人员日常行为检测的分类模型,再与指纹库中的数据进行匹配。实验结果表明,所提方法具有高效率、高精度、顽健性较好等特点,且无需测试人员携带任何电子设备,实用性较高。

关键词: 信道状态信息, 行为检测, 低通滤波, 支持向量机

Abstract:

The daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoor human behavior detection method HDFi (Human Detection with Wi-Fi) based on CSI signal was proposed.The method was used to detect the indoor human daily behavior in a 5 GHz band environment,which was divided into three steps:data acquisition,data processing,feature extraction,online detection.Firstly,the experiment collected typical daily behavioral data in complex laboratory and relatively empty meeting room.Secondly,the amplitude and phase data with more obvious features were extracted and processed by low-pass filtering to obtain a set of stable and noise-free data,and then the fingerprint database was established effectively.Finally,in the real-time detection stage,the collected data features were classified by SVM algorithm to extract more stable eigenvalues,and a classification model of indoor human daily behavior detection was established,and then matched the data in the fingerprint database.The experimental results show that the proposed method has the characteristics of high efficiency,high precision and good robustness,and the method does not need any testing personnel to carry any electronic equipment,so it has high practicability.

Key words: channel state information, behavior detection, low pass filtering, support vector machine

中图分类号: 

  • TP391