Data Driven Sensor Model for Wi-Fi Fine Timing Measurement

Abstract

Radio frequency ranging protocols enable a device to estimate the distance to another device, based on signal propagation time. In theory, ranging protocols are promising for indoor localization as one can obtain the position of the pedestrian directly from the ranging results if the access point positions are known. However, in practice, indoor scenarios still pose a challenging problem as the observed distances vary greatly due to non-line-of-sight signal paths, delayed signal propagation, and general hardware inaccuracies. The IEEE 802.11-2016 (formerly IEEE 802.11mc) standard defines a RF ranging protocol for Wi-Fi, namely Fine Timing Measurement (FTM). In order to improve the position estimate, a novel sensor model for FTM is derived from observed data. It is shown that the FTM error varies with the actual distance to the access point. Within this work, different parameter sets are estimated from the observed data for skew normal distributions, depending on the actual distance. For these parameters, low-order polynomials are then fitted to obtain the distribution parameters as functions of the actual distance. Furthermore, a particle filter is described and evaluated in an industrial scenario using cheap Espressif ESP32-S2 IoT FTM access points in the 2.4 GHz band. The filter combines map information, Pedestrian Dead Reckoning, and our novel FTM sensor model to estimate the pedestrian's position in the building. Finally, the localization result of the particle filter is compared to another promising radio frequency ranging method: ultra-wideband.