A Taxonomy of WiFi Sensing: CSI vs Passive WiFi Radar

Wenda Li*, Mohammud J Bocus, Chong Tang, Shelly Vishwakarma, Robert J Piechocki, Karl Woodbridge, Kevin Chetty

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

WiFi sensing has shown promising potentials in a number of applications such as healthcare, smart transportation and home automation. Human activity recognition without the use of any cooperative device such as phones or wearable technologies can be achieved by two WiFi based approaches: Channel State Information (CSI) and Passive WiFi Radar (PWR). CSI systems rely directly on WiFi as a communications system, whilst PWR treats the WiFi signal as an illuminator for use in a radar signal processing. However, there has not been a comprehensive comparative study on the similarities and differences between the two systems. To examine the performance of both systems we implement two hardware platforms for CSI and PWR, and use them concurrently to capture the human movements. In this paper, we present Doppler measurements from the two systems and compare their performance using a dataset obtained from five subjects undergoing six activity classes. It is observed that both systems have very different Doppler signatures, and are sensitive to the transmitter-target-receiver geometries. CSI has a better performance in Line-of-Sight (LoS) configurations, whereas PWR has better performance in bistatic configurations where the WiFi access point and radar receiver are spatially separated. It is envisioned that a more robust system should leverage strengths of both the CSI and PWR systems jointly to maximize their benefits in wireless sensing.
Original languageEnglish
Title of host publicationIEEE Global Communications (GLOBECOM) Conference Workshop 2020 on Integrated Sensing And Communication, Taipei, Taiwan, 7-11 Dec. 2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
Publication statusAccepted/In press - 19 Sep 2020

Structured keywords

  • Digital Health

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