LowDetrack: A Human Detection and Tracking System for Wi-Fi Low Packet Rates

Aiwen Yu, Cheng Yuan, Chenwen Gao, Xinyu Tong, Xiulong Liu, Xin Xie, Jiancheng Chen, Keqiu Li

Abstract: The Wi-Fi sensing technique holds great promise for future smart homes, thanks to the widespread use of Wi-Fi devices. With this technique, we can deduce the behavior of the target based on the channel state information (CSI), which is obtained during Wi-Fi communication. However, existing Wi-Fi sensing technologies are not compatible with standard communication technologies. This is because Wi-Fi sensing usually relies on capturing CSI from high-frequency communication packets, whereas regular IoT communication does not consistently maintain such high communication rates. To achieve precise sensing even with a low packet rate, we introduce LowDetrack, an indoor human detection and tracking system at ultralow packet rates with Wi-Fi. In particular, we utilize compressed sensing (CS) to supplement missing data compared to existing systems that rely on linear interpolation or neural networks. To detect and track the target, our insights are twofold: 1) We combine CS and Fresnel zone to a theoretical model for accurately obtaining the reflection path change rate, which can be converted into the actual velocity of the target and 2) We investigate the mapping relationship between the dynamic frequency composition ratios in different links, which can provide navigation for velocity direction and correct direction recognition errors. We implement LowDetrack on commercial off-the-shelf Wi-Fi and realize human detection and tracking, where the median tracking error is $0.76~{\mathrm { m}}$ at the packet rate of 25 Hz.

External IDs:dblp:journals/iotj/YuYGTLXCL25