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Gesture awareness plays a crucial role in promoting human-computer interface.Previous works either depend on customized hardware or need a priori learningof wireless signal patterns, facing downsides in terms of the privacy concern,availability and reliability. In this paper, we propose CentiTrack, the firstcentimeter-level passive gesture tracking system that works with only threecommodityWiFi devices, without any extra hardware modifications or wearablesensors. To this end, we first identify the Channel State Information (CSI)measurement error sources in the physical layer process, and then denoise CSIby the complex ratio between adjacent antennas. Principal Component Analysis(PCA) is further adopted to separate the reflected signals from noises.Benchmark experiments are conducted to verify that the phase changes ofdenoised CSI are proportional to the length changes of dynamic path reflectedoff the hand. In addition, we adopt the Multiple Signal Classification (MUSIC)algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and thenlocate the initial position of hands with triangulation. We also propose anovel static componnets elimination algorithm for tracking correction byeliminating the components unrelated to motion. A prototype of CentiTrack isfully realized and evaluated in various real scenarios. Extensive experimentsshow that CentiTrack is superior in terms of tracking accuracy, sensing rangeand device cost, compared with the state-of-the-arts.

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi