R-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human With WiFi

• Published in: IEEE journal on selected areas in communications 2017-05, Vol.35 (5), p.1090-1103
• Main Authors: Zhu, Hai, Xiao, Fu, Sun, Lijuan, Wang, Ruchuan, Yang, Panlong
• Format: Article
• Language: English
• Subjects: Applications programs; channel state information (CSI); Commodities; Device-free; Electronic devices; Feature extraction; Filtration; human detection; Interactive systems; Mobile computing; Performance evaluation; Robustness; Sensors; Subcarriers; through-the-wall; Transceivers; WiFi; Wireless communication; Wireless fidelity; Wireless sensor networks
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2017.2679578

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human-computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit-receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.