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

TL;DR

WiVelo introduces a novel Wi-Fi passive tracking method that accurately estimates walking velocity using spatial-temporal signal correlation features, significantly improving tracking accuracy over existing Doppler-based approaches.

Contribution

The paper proposes a new velocity estimation technique based on subcarrier shift distribution and a mesh model, enabling fine-grained, bounded-error velocity estimation for passive human tracking.

Findings

Median tracking error of 0.47 m

90% tracking error of 1.06 m

Outperforms state-of-the-art methods by 50% and 75% in accuracy

Abstract

Passive human tracking via Wi-Fi has been researched broadly in the past decade. Besides straight-forward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS) which suffers from the inevitable signal noise incurring 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 new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas' locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90\% tracking errors are 0.47~m and 1.06~m, which are half and a quarter of state-of-the-arts.