Indoor Localization of a Moving Mobile Terminal by an Enhanced Particle Filter Method

  • Michał Okulewicz
  • Dominika Bodzon
  • Marek Kozak
  • Michał Piwowarski
  • Patryk Tenderenda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9693)

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 real-world 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 \(15\,\%\).

Keywords

Particle Filter Random Forest Particle Swarm Optimization Machine learning Hidden Markov models On-line mobile phone localization 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michał Okulewicz
    • 1
  • Dominika Bodzon
    • 1
  • Marek Kozak
    • 1
  • Michał Piwowarski
    • 1
  • Patryk Tenderenda
    • 1
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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