A particle filter for Wi-Fi azimuth and position tracking with pedestrian dead reckoning

Abstract

A tracking algorithm for estimating the azimuth angle regarding north and a two-dimensional position of a mobile unit carried by a pedestrian is presented. Using Wi-Fi signal strength measurements the position of a mobile receiver can be estimated using so called fingerprinting methods. If the signal strengths measurements are collected with directional antennas additionally the azimuth can be estimated. For sensor data fusion of Wi-Fi signal strength measurements, acceleration measurements and angular rate measurements a particle filter is presented. The well known Wi-Fi fingerprinting approach is used to calculate the particle weights and pedestrian dead reckoning to sample the particles. Measurements have been collected inside and outside of an office building to evaluate the performance. Including step detection based on acceleration measurements reduces mainly the positioning error, including angular rate measurements reduces mainly the azimuth estimation error. Electronic compasses, which are susceptible to faults, are not needed to estimate the azimuth indoors. Especially in indoor environments this approach facilitates the use of electronic guides that offer additional information by means of augmented reality, e.g. on museum exhibits in visual range.