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Open Access

Robust and Passive Motion Detection with COTS WiFi Devices

College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education and College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
College of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China.
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Abstract

Device-free Passive (DfP) detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength (RSS), most recent studies rely on finer-grained Channel State Information (CSI). However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight (LOS) or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection (R-PMD). Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis (PCA)-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

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Tsinghua Science and Technology
Pages 345-359
Cite this article:
Zhu H, Xiao F, Sun L, et al. Robust and Passive Motion Detection with COTS WiFi Devices. Tsinghua Science and Technology, 2017, 22(4): 345-359. https://doi.org/10.23919/TST.2017.7986938

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Received: 19 September 2016
Accepted: 21 October 2016
Published: 20 July 2017
© The author(s) 2017
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