Indoor localization of a moving mobile terminal by an enhanced particle filter method

Abstract

This article presents a method of localizing a moving mobile terminal (i.e. phone) with the usage of the Particle Filter method. The method is additionally enhanced with the predictions done by a Random Forest and the results are optimized with the usage of the Particle Swarm Optimization algorithm. The method proposes a simple model of movement through the building, a likelihood estimation function for evaluating locations against the observed signal, and a method of generating multiple location propositions from a single point prediction statistical model on the basis of model error estimation. The method uses a data set of the GSM and WiFi networks received signals’ strengths labeled with a receiver’s 3D location. The data have been gathered in a six floor building. The approach is tested on a realworld data set and compared with a single point estimation performed by a Random Forest. The Particle Filter approach has been able to improve floor recognition accuracy by around 7% and lower the median of the horizontal location error by around 15 %.