WiVelo: Fine-grained Walking Velocity Estimation for Wi-Fi Passive Tracking
Chenning Li, Li Liu, Zhichao Cao, Mi Zhang

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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Millimeter-Wave Propagation and Modeling · Advanced Adaptive Filtering Techniques
