EasyWiTrack: Fine-Grained Sensing for Plug-and-Play Position Tracking with Wi-Fi

Previous work have verified the feasibility of Wi-Fi-based indoor position tracking. However, these research rely heavily on anchors' position as prior knowledge, which lowers the deployability in practical scenarios. To this end, we propose a novel system named EasyWiTrack to realize a plug-and-play, relative position tracking by establishing a time-domain correlation model to associate the target path, angle information with target motion, and deduce the target's relative position compared to the previous moment for trajectory shape without any anchor position information. During the system implementation, to address the issue of error introduced by asynchronous Wi-Fi transceivers, we adopt bi-directional data acquisition for denoising and achieve millimeter-level target path length estimation. Additionally, to eliminate ambiguity in linear array angle estimation, we employ circle array and apply Multiple Signal Classification algorithm to estimate signal azimuth. Finally through extensive experimentation, we have validated that EasyWiTrack achieves a fine-grained indoor position tracking performance with 1.48cm and 1.73cm average error respectively in LoS and NLoS scenarios with commodity Wi-Fi.