Passively Track WiFi Users With an Enhanced Particle Filter Using Power-Based Ranging

Abstract

Passive positioning systems produce user location information for third-party providers of positioning services. In this paper, 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-bootstrap particle filter, provides 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 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 (0.12 s for 1000 particles) only half of the computation efforts as a multi-model particle filter.