  Gesture awareness plays a crucial role in promoting human-computer interface.
Previous works either depend on customized hardware or need a priori learning
of wireless signal patterns, facing downsides in terms of the privacy concern,
availability and reliability. In this paper, we propose CentiTrack, the first
centimeter-level passive gesture tracking system that works with only three
commodityWiFi devices, without any extra hardware modifications or wearable
sensors. To this end, we first identify the Channel State Information (CSI)
measurement error sources in the physical layer process, and then denoise CSI
by 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 of
denoised CSI are proportional to the length changes of dynamic path reflected
off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC)
algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then
locate the initial position of hands with triangulation. We also propose a
novel static componnets elimination algorithm for tracking correction by
eliminating the components unrelated to motion. A prototype of CentiTrack is
fully realized and evaluated in various real scenarios. Extensive experiments
show that CentiTrack is superior in terms of tracking accuracy, sensing range
and device cost, compared with the state-of-the-arts.
