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Passive WiFi Radar for Human Sensing Using a Stand-Alone Access Point.

Authors :
Li, Wenda
Piechocki, Robert J.
Woodbridge, Karl
Tang, Chong
Chetty, Kevin
Source :
IEEE Transactions on Geoscience & Remote Sensing. Mar2021, Vol. 59 Issue 3, p1986-1998. 13p.
Publication Year :
2021

Abstract

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
59
Issue :
3
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
149616062
Full Text :
https://doi.org/10.1109/TGRS.2020.3006387