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Passive human tracking via Wi-Fi has been researched broadly in the pastdecade. Besides straight-forward anchor point localization, velocity is anothervital sign adopted by the existing approaches to infer user trajectory.However, state-of-the-art Wi-Fi velocity estimation relies onDoppler-Frequency-Shift (DFS) which suffers from the inevitable signal noiseincurring unbounded velocity errors, further degrading the tracking accuracy.In this paper, we present WiVelo\footnote{Code\&datasets are available at\textit{https://github.com/liecn/WiVelo\_SECON22}} that explores newspatial-temporal signal correlation features observed from different antennasto achieve accurate velocity estimation. First, we use subcarrier shiftdistribution (SSD) extracted from channel state information (CSI) to define twocorrelation features for direction and speed estimation, separately. Then, wedesign a mesh model calculated by the antennas' locations to enable afine-grained velocity estimation with bounded direction error. Finally, withthe continuously estimated velocity, we develop an end-to-end trajectoryrecovery algorithm to mitigate velocity outliers with the property of walkingvelocity continuity. We implement WiVelo on commodity Wi-Fi hardware andextensively evaluate its tracking accuracy in various environments. Theexperimental results show our median and 90\% tracking errors are 0.47~m and1.06~m, which are half and a quarter of state-of-the-arts.

WiVelo: Fine-grained Walking Velocity Estimation for Wi-Fi Passive Tracking