基于信道状态信息的安防监控系统

计算机与现代化 ›› 2021, Vol. 0 ›› Issue (11): 118-126.

• 模式识别 • 上一篇

基于信道状态信息的安防监控系统

(甘肃农业大学信息科学技术学院，甘肃兰州730070)

出版日期:2021-12-13 发布日期:2021-12-13
作者简介:王历（1998—），男，甘肃天水人，本科生，研究方向：电子信息工程，E-mail: 1158010538@qq.com；孙选辰（1999—），男，河南濮阳人，本科生，研究方向：电子信息工程；姜少维（1999—），男，甘肃天水人，本科生，研究方向：电子信息工程；俄万林（1995—），男，甘肃庆阳人，本科生，研究方向：电子信息工程；李佳康（1999—），男，河南濮阳人，本科生，研究方向：电子信息工程；通信作者：逯玉兰，副教授，研究方向：人工智能，农业信息技术，E-mail: luyl@gsau.edu.cn。
基金资助:
国家自然科学基金资助项目(61741201)；甘肃省重点研发计划项目(20YF8NA135)；甘肃农业大学盛彤笙基金资助项目（GSAU-STS-2021-17）；甘肃农业大学省级大学生创新创业训练计划项目(S202110733045)；甘肃农业大学青年导师扶持基金资助项目(GAU-QDFC-2020-13)；甘肃农业大学学生科研训练项目SRTP(202116001)

Security Monitoring System Based on Channel State Information

(College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China)

Online:2021-12-13 Published:2021-12-13

摘要/Abstract

摘要： 无线网络的信道状态信息（Channel State Information, CSI）是MIMO系统中反映各个子载波信号频谱特性的重要指标，通过在Linux平台上定制Intel 5300网卡驱动，就可在常规的无线网络通信环境下通过编程实现对CSI数据的实时采集。因CSI信息对环境变化十分敏感，因此可用于构建基于CSI的非接触式环境感知系统。本文以CSI为数据载体，详细梳理基于CSI的无线感知应用，建立基于CSI信号的安防监控系统，能够成功检测人员到访事件。系统提取人员到访时间的CSI信号特征〖WTHX〗v〖WTBX〗*t={σ,MaxPt,MinPt,E}并通过实验求得，当其值为〖WTHX〗v〖WTBX〗*t={1×10－3,［1,2］,［1,2］,2.0×10－4}时，系统可获得高达99.07%的检测成功率。

关键词: 接收信号强度指标, 正交频分多路复用技术, 信道状态信息（CSI）, 安防监控, 无线感知

Abstract: CSI is an important index reflecting the spectrum characteristics of each subcarrier signal in the MIMO system. By customizing the Intel 5300 network card driver on the Linux platform, real-time CSI data acquisition can be realized through programming under the conventional wireless network communication environment. CSI information is very sensitive to environmental changes, so it can be used to build a non-contact environment sensing system based on CSI. This paper takes channel state information as data carrier, reviews related applications based on CSI in detail, and establishes an intrusion detection system. The system can successfully detect personnel visit events. The system defines a vector 〖WTHX〗v〖WTBX〗*t={σ, MaxPt, MinPt, E} as the intrusion feature and obtains the value of 〖WTHX〗v〖WTBX〗*t as {1×10－3,［1,2］,［1,2］,2.0×10－4} through experiments. Meanwhile, when the size of sliding window is set to 10 seconds, the system can achieve a detection success rate of 99.07%.

Key words: RSSI, OFDM, CSI, security monitoring, wireless sensing

王历, 孙选辰, 姜少维, 俄万林, 李佳康, 逯玉兰. 基于信道状态信息的安防监控系统[J]. 计算机与现代化, 2021, 0(11): 118-126.

WANG Li, SUN Xuan-chen, JIANG Shao-wei, E Wan-lin, LI Jia-kang, LU Yu-lan. Security Monitoring System Based on Channel State Information[J]. Computer and Modernization, 2021, 0(11): 118-126.

参考文献

［1］ LV J G, YANG W, GONG L Y, et al. Robust WLAN-based indoor fine-grained intrusion detection［C］// 2016 IEEE Global Communications Conference. 2016. DOI: 10.1109/GLOCOM.2016.7842238.
［2］朱海,肖甫,孙力娟,等. 基于信道状态信息的WiFi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.
［3］ WU K S, XIAO J, YI Y W, et al. FILA： Fine-grained indoor localization［C］// 2012 IEEE INFOCOM Proceedings. 2012:2210-2218.
［4］ YANG Z, ZHOU Z M, LIU Y H. From RSSI to CSI： Indoor localization via channel response［J］. ACM Computing Surveys, 2013,46(2). DOI: 10.1145/2543581.2543592.
［5］ MOLISCH A F. 无线通信［M］. 田斌,帖翊,任光亮，译. 2版. 北京:电子工业出版社, 2015:56-63.
［6］ HALPERIN D, HU W J, SHETH A, et al. Predictable 802.11 packet delivery from wireless channel measurements［J］. ACM SIGCOMM Computer Communication Review, 2010,40(4):159-170.
［7］ XU C C, JIA Z X, CHEN P, et al. CSI-based autoencoder classification for Wi-Fi indoor localization［C］// 2016 Chinese Control and Decision Conference. 2016:6523-6528.
［8］ WANG X Y, GAO L J, MAO S W. BiLoc: Bi-modal deep learning for indoor localization with commodity 5 GHz WiFi［J］. IEEE Access, 2017,5:4209-4220.
［9］ XIAO J, WU K S, YI Y W, et al. Pilot: Passive device-free indoor localization using channel state information［C］// 2013 IEEE 33rd International Conference on Distributed Computing Systems. 2013:236-245.
［10］黄华晋,秦建息. 基于三角定位算法的WiFi室内定位系统设计［J］. 广西科学院学报, 2016,32(1):59-61.
［11］ABDELNASSER H, YOUSSEF M, HARRAS K A. WiGest： A ubiquitous WiFi-based gesture recognition system［C］// 2015 IEEE Conference on Computer Communications. 2015:1472-1480.
［12］AL-QANESS M, LI F M, MA X L, et al. Device-free indoor activity recognition system［J］. Applied Sciences, 2016,6(11). DOI: 10.3390/app6110329.
［13］ZENG Y Z, PATHAK P H, MOHAPATRA P. WiWho: WiFi-based person identification in smart spaces［C］// 2016 ACM/IEEE International Conference on Information Processing in Sensor Networks. 2016. DOI: 10.1109/IPSN.2016.7460727.
［14］XU Q Y, CHEN Y, WANG B B, et al. Radio biometrics: Human recognition through a wall［J］. IEEE Transactions on Information Forensics and Security, 2017,12(5):1141-1155.
［15］WU K S. Wi-metal: Detecting metal by using wireless networks［C］// 2016 IEEE International Conference on Communications. 2016:4842-4847.
［16］ALI K, LIU A X, WANG W, et al. Keystroke recognition using WiFi signals［C］// 2015 ACM International Conference on Mobile Computing and Networking. 2015:90-102.
［17］WANG X Y, YANC C, MAO S W. TensorBeat: Tensor decomposition for monitoring multiperson breathing beats with commodity WiFi［J］. ACM Transactions on Intelligent Systems & Technology, 2017,9(1). DOI: 10.1145/3078855.
［18］CHANG J Y, LEE K Y, WEI Y L, et al. We can “see ”you via WiFi: An overview and beyond［J］. arXiv preprint arXiv:1608.05461, 2016.
［19］ZHENG X L, WANG J L, SHANGGUAN L F, et al. Smokey： Ubiquitous smoking detection with commercial WiFi infrastructures［C］// 2016 IEEE International Conference on Computer Communications. 2016. DOI:10.1109/INFOCOM.2016.7524399.
［20］IEEE 802.11g. IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements［S］. NewYork: I.C. Society, 2009.
［21］张六,曾正,陈俊昌,等. 基于信道状态信息的室内人数统计方法［J］. 计算机工程与设计, 2020,41(4):1125-1132.
［22］江小平,王妙羽,丁昊,等. 基于信道状态信息幅值-相位的被动式室内指纹定位［J］. 电子与信息学报, 2020,42(5):1165-1171.
［23］徐捷,周言,吴哲夫. 基于信道状态的室内定位技术研究［J］. 电声技术, 2015,39(10):69-72.

基于信道状态信息的安防监控系统

Security Monitoring System Based on Channel State Information

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价