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)


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 \(15\,\%\).


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



The research is supported by the National Centre for Research and Development, grant No PBS2/B3/24/2014, application No 208921.


  1. 1.
    Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)CrossRefGoogle Scholar
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Clerc, M.: Standard PSO 2011 (2012).
  4. 4.
    Cristian, I.: Trelea: the particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)CrossRefMATHGoogle Scholar
  5. 5.
    Del Moral, P.: Nonlinear filtering: interacting particle solution. Markov Processes Relat. Fields 2(4), 555–580 (1996)MathSciNetMATHGoogle Scholar
  6. 6.
    Górak, R., Luckner, M.: Malfunction immune Wi-Fi localisation method. In: Nez, M., Nguyen, N., Camacho, D., Trawiski, B. (eds.) Computational Collective Intelligence. LNCS, vol. 9329, pp. 328–337. Springer, Heidelberg (2015). CrossRefGoogle Scholar
  7. 7.
    Grzenda, M.: On the prediction of floor identification credibility in RSS-based positioning techniques. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) IEA/AIE 2013. LNCS, vol. 7906, pp. 610–619. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  8. 8.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  9. 9.
    Karwowski, J., Okulewicz, M., Legierski, J.: Application of particle swarm optimization algorithm to neural network training process in the localization of the mobile terminal. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013. Communications in Computer and Information Science, vol. 383, pp. 122–131. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  11. 11.
    Korbel, P., Wawrzyniak, P., Grabowski, S., Krasinska, D.: LocFusion API - programming interface for accurate multi-source mobile terminal positioning. In: 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 819–823, September 2013Google Scholar
  12. 12.
    Okulewicz, M., Mańdziuk, J.: Dynamic vehicle routing problem: a Monte Carlo approach. In: Proceedings of the Selected Problems in Information Technologies, ITRIA 2015, vol. 1, pp. 119–138. ICS PAS (2015).
  13. 13.
    Papapostolou, A., Chaouchi, H.: Scene analysis indoor positioning enhancements. Ann. Télécommunications 66, 519–533 (2011)CrossRefGoogle Scholar
  14. 14.
    Rabiner, L., Juang, B.: An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)CrossRefGoogle Scholar
  15. 15.
    Roos, T., Myllymaki, P., Tirri, H., Misikangas, P., Sievanen, J.: A probabilistic approach to WLAN user location estimation. Int. J. Wireless Inf. Netw. 9(3), 155–164 (2002)CrossRefGoogle Scholar
  16. 16.
    Rosłan, A.: LOKKOM data viewing interface (in Polish) (2014).
  17. 17.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)Google Scholar
  18. 18.
    Shi, Y., Eberhart, R.: Parameter selection in particle swarm optimization. In: Proceedings of Evolutionary Programming VII (EP98), pp. 591–600 (1998)Google Scholar
  19. 19.
    Wang, J., Hu, A., Liu, C., Li, X.: A floor-map-aided WiFi/pseudo-odometry integration algorithm for an indoor positioning system. Sensors 15(4), 7096 (2015). CrossRefGoogle Scholar
  20. 20.
    Wawrzyniak, P., Hausman, S., Korbel, P.: Sequence detection of movement for accurate area based indoor positioning and tracking. In: 2015 9th European Conference on Antennas and Propagation (EuCAP), pp. 1–4, May 2015Google Scholar
  21. 21.
    Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J., Gao, X.G.: A wireless LAN-based indoor positioning technology. IBM J. Res. Dev. 48(5–6), 617–626 (2004)CrossRefGoogle Scholar

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

Personalised recommendations