Passive Human Tracking With WiFi Point Clouds

Integrated sensing and communication (ISAC) technology empowers WiFi to function as both sensors for wireless sensing and communication devices for data exchange. Currently, achieving accurate object tracking with commercial WiFi devices is still challenging due to the limited bandwidth, a small number of antennas, and the clock asynchronization in a bi-static setup. Many existing methods achieve tracking only via extracting a dominant Doppler frequency shift (DFS) from a moving person. However, since the human body is nonrigid, various body parts generate different DFSs, and different subcarriers can exhibit varying Doppler characteristics in a multipath environment. This work presents WiDFS2.0, an enhanced real-time tracking scheme that leverages the micro-Doppler effect to extract multiple signal features from various body parts of a moving person, represented as WiFi point clouds. Each point cloud consists of Doppler, Angle of Arrival, Range, and signal-to-noise ratio. We design a novel signal processing chain to extract the WiFi point clouds. Then, we refine these point clouds and implement an extended Kalman filter-based algorithm to track the person’s trajectory. Our experiments demonstrate that WiDFS2.0 can achieve real-time tracking with a median position error of 0.55 m, while determining the presence of a moving person with over 98% accuracy during tracking.