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; 黄亚宁; 郝占军; 司雄

doi:10.11959/j.issn.1000-436x.2019082

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基于信道状态信息的无源室内人员日常行为检测方法

学术论文 | 更新时间：2024-06-05

- 基于信道状态信息的无源室内人员日常行为检测方法
- Passive indoor human daily behavior detection method based on channel state information
- 通信学报 2019年40卷第4期页码：160-170
- 作者机构：
  
  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
- 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。" ]
- 基金信息：
  
  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)
- DOI：10.11959/j.issn.1000-436x.2019082
  中图分类号： TP391
- 网络出版日期：2019-04，
  
  纸质出版日期：2019-04-25
- 稿件说明：
移动端阅览
党小超, 黄亚宁, 郝占军, 等. 基于信道状态信息的无源室内人员日常行为检测方法[J]. 通信学报, 2019,40(4):160-170.

Xiaochao DANG, Yaning HUANG, Zhanjun HAO, et al. Passive indoor human daily behavior detection method based on channel state information[J]. Journal on communications, 2019, 40(4): 160-170.
党小超, 黄亚宁, 郝占军, 等. 基于信道状态信息的无源室内人员日常行为检测方法[J]. 通信学报, 2019,40(4):160-170. DOI： 10.11959/j.issn.1000-436x.2019082.

Xiaochao DANG, Yaning HUANG, Zhanjun HAO, et al. Passive indoor human daily behavior detection method based on channel state information[J]. Journal on communications, 2019, 40(4): 160-170. DOI： 10.11959/j.issn.1000-436x.2019082.

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。

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.

关键词

Keywords

references

周牧 , 蒲巧林 , 田增山 . 室内 WLAN 定位中位置指纹优化的接入点部署方法 [J ] . 通信学报 , 2015 , 36 ( S1 ): 30 - 41 .

ZHOU M , PU Q L , TIAN Z S . Location fingerprint optimization based access point deployment in indoor WLAN localization [J ] . Journal on Communications , 2015 , 36 ( S1 ): 30 - 41 .

鲁勇 , 吕绍和 , 王晓东 , 等 . 基于 Wi-Fi 信号的人体行为感知技术研究综述 [J ] . 计算机学报 , 2019 , 42 ( 2 ): 1 - 21 .

LU Y , LYV S H , WANG X D , et al . A survey on Wi-Fi based human behavior analysis technology [J ] . Chinese Journal of Computers , 2019 , 42 ( 2 ): 1 - 21 .

朱海 , 肖甫 , 孙力娟 , 等 . 基于信道状态信息的 Wi-Fi 环境感知技术 [J ] . 南京邮电大学学报(自然科学版) , 2016 , 36 ( 1 ): 94 - 103 .

ZHU H , XIAO F , SUN L J , et al . CSI-based Wi-Fi environment sensing [J ] . Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition) , 2016 , 36 ( 1 ): 94 - 103 .

YOUSSEF M , MAH M , AGRAWALA A . Challenges:device-free passive localization for wireless environments [C ] // International Conference on Mobile Computing and Networking . ACM , 2007 : 222 - 229 .

PENG H , JIA W . Wi-Find:driver fatigue detection with fine-grained Wi-Fi signal features [C ] // IEEE Global Communications Conference . IEEE , 2017 : 1 - 6 .

HALPERIN D , HU W , SHETH A , et al . Predictable 802.11 packet delivery from wireless channel measurements [J ] . ACM SIGCOMM Computer Communication Review , 2010 , 40 ( 4 ): 159 - 170 .

SHANG J , WU J . A robust sign language recognition system with sparsely labeled instances using Wi-Fi signals [C ] // International Conference on Mobile Ad Hoc and Sensor Systems . IEEE , 2017 : 99 - 107 .

PANG N , ZHU D , LI G , et al . WarnFi:non-invasive Wi-Fi-based abnormal activity sensing using non-parametric model [C ] // IEEE Military Communications Conference . IEEE , 2017 : 800 - 805 .

LAI W C , . Neural switched capacitor low-pass filter for wearable human detection and microwave generation [C ] // International Conference on Optical Communications and Networks . IEEE , 2015 : 1 - 3 .

ZHOU R , LU X , ZHAO P , et al . Device-free presence detection and localization with SVM and CSI fingerprinting [J ] . IEEE Sensors Journal , 2017 , 17 ( 23 ): 7990 - 7999 .

WANG W , LIU A X , SHAHZAD M , et al . Understanding and modeling of Wi-Fi signal based human activity recognition [C ] // International Conference on Mobile Computing and Networking . ACM , 2015 : 65 - 76 .

WANG G , ZOU Y , ZHOU Z , et al . We can hear you with Wi-Fi! [C ] // International Conference on Mobile Computing and Networking . ACM , 2014 : 593 - 604 .

XIAO J , WU K , YI Y , et al . FIMD:fine-grained device-free motion detection [C ] // The International Conference on Parallel and Distributed Systems . IEEE , 2012 90 ( 1 ): 229 - 235 .

ZHU H , XIAO F , SUN L , et al . Robust and passive motion detection with COTS Wi-Fi devices [J ] . Tsinghua Science and Technology , 2017 , 22 ( 4 ): 345 - 359 .

QIAN K , WU C , YANG Z , et al . Widar:decimeter-level passive tracking via velocity monitoring with commodity Wi-Fi [C ] // The ACM International Symposium . ACM , 2017 : 1 - 10 .

CHRIKI A , TOUATI H , SNOUSSI H . SVM-based indoor localization in wireless sensor networks [C ] // Wireless Communications and Mobile Computing Conference . IEEE , 2017 : 1144 - 1149 .

钱志鸿 , 刘丹 . 蓝牙技术数据传输综述 [J ] . 通信学报 , 2012 , 33 ( 4 ): 143 - 151 .

QIAN Z H , LIU D . Survey on data transmission in bluetooth technology [J ] . Journal on Communications , 2012 , 33 ( 4 ): 143 - 151 .

刘文军 , 樊建席 , 李春胜 , 等 . 基于 ZigBee 无线传感器网络的智能交通系统设计 [J ] . 传感技术学报 , 2013 ( 12 ): 1747 - 1751 .

LIU W J , FAN J X , LI C S , et al . An intelligent transportation system supported by ZigBee-based wireless sensor network [J ] . Chinese Journal of Sensors and Actuators , 2013 ( 12 ): 1747 - 1751 .

肖玲 , 潘浩 . 基于Wi-Fi信号的人体动作识别系统 [J ] . 北京邮电大学学报 , 2018 , 41 ( 3 ): 119 - 124 .

XIAO L , PAN H . Human activity recognition system based on Wi-Fi signal [J ] . Journal of Beijing University of Posts and Telecommunications , 2018 , 41 ( 3 ): 119 - 124 .

QIAN K , WU C , YANG Z , et al . PADS:passive detection of moving targets with dynamic speed using PHY layer information [C ] // International Conference on Parallel and Distributed Systems . IEEE Computer Society , 2014 : 1 - 8 .

LI H , YANG W , WANG J , et al . Wi-Finger:talk to your smart devices with finger-grained gesture [C ] // ACM International Joint Conference on Pervasive and Ubiquitous Computing . ACM , 2016 : 250 - 261 .

WU C , YANG Z , ZHOU Z , et al . PhaseU:real-time LOS identification with Wi-Fi [C ] // Computer Communications . IEEE , 2015 : 2038 - 2046 .

HU H , LI L . A new method using covariance eigenvalues and time window in passive human motion detection based on CSI phases [C ] // International Symposium on Electromagnetic Compatibility . IEEE , 2017 : 1 - 6 .

ZHANG L , DING E , ZHAO Z , et al . A novel fingerprinting using channel state information with MIMO-OFDM [J ] . Cluster Computing , 2017 , 20 ( 4 ): 1 - 14 .

TAN S , YANG J . Wi-Finger:leveraging commodity Wi-Fi for fine-grained finger gesture recognition [C ] // International Symposium on Mobile Ad Hoc Networking and Computing . ACM , 2016 : 201 - 210 .

XIE Y , LI Z , LI M . Precise power delay profiling with commodity Wi-Fi [C ] // International Conference on Mobile Computing and Networking . ACM , 2015 : 53 - 64 .

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