Baton: Compensate for Missing Wi-Fi Features for Practical Device-free Tracking

TL;DR

Baton is a Wi-Fi sensing system that maintains accurate device-free tracking despite severe feature deficiencies by leveraging feature correlations across links and time, using the STAP algorithm, and demonstrating significant error reduction.

Contribution

This work introduces Baton, the first system capable of robust tracking under Wi-Fi feature deficiencies by exploiting feature correlations and the STAP algorithm.

Findings

Median tracking error of 0.46m achieved

Outperforms existing solutions with 79.19% error reduction

Operates effectively with only 20% communication duty cycle

Abstract

Wi-Fi contact-free sensing systems have attracted widespread attention due to their ubiquity and convenience. The integrated sensing and communication (ISAC) technology utilizes off-the-shelf Wi-Fi communication signals for sensing, which further promotes the deployment of intelligent sensing applications. However, current Wi-Fi sensing systems often require prolonged and unnecessary communication between transceivers, and brief communication interruptions will lead to significant performance degradation. This paper proposes Baton, the first system capable of accurately tracking targets even under severe Wi-Fi feature deficiencies. To be specific, we explore the relevance of the Wi-Fi feature matrix from both horizontal and vertical dimensions. The horizontal dimension reveals feature correlation across different Wi-Fi links, while the vertical dimension reveals feature correlation among different time slots. Based on the above principle, we propose the Simultaneous Tracking And Predicting (STAP) algorithm, which enables the seamless transfer of Wi-Fi features over time and across different links, akin to passing a baton. We implement the system on commercial devices, and the experimental results show that our system outperforms existing solutions with a median tracking error of 0.46m, even when the communication duty cycle is as low as 20.00%. Compared with the state-of-the-art, our system reduces the tracking error by 79.19% in scenarios with severe Wi-Fi feature deficiencies.