Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Li, Zan; Braun, Torsten (2017). Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging IEEE Transactions on Wireless Communications 10.1109/TWC.2017.2746598

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Passive positioning
systems produce user location
information for third-party providers of positioning services.
In this work, we provide a passive tracking system for WiFi
signals with an enhanced range-only particle filter using fine-
grained power. Our proposed particle filter, WVT-BPF, provides
an improved observation likelihood and is equipped with a
single coordinated turn model to address the challenges in
passive positioning. The anchor nodes for WiFi signal sniffing
use software defined radio techniques to extract Channel State
Information (CSI) for multipath mitigation and a non-linear
regression method is used for the path-loss model. Our tracking
system produces measured positioning errors that, in the 80th
percentile, are equal to or less than 2 m; this represents a
33% improvement over the traditional bootstrap particle filter.
Additionally, it requires (0.12s for 1000 particles) only half of the
computation effort as a multi-model particle filter.

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