Abstract
Indoor localisation systems based on a Wi–Fi local area wireless technology bring constantly improving results. However, the whole localisation system may fail when one or more Access Point (AP) malfunctions. In this paper we present how to limit the number of observed APs and how to create a malfunction immune localisation method. The presented solutions are an ensemble of random forests with an additional malfunction detection system. The proposed solution reduces a growth of the localisation error to 4 percent for the floor detection inside a six floor building and 2 metres for the horizontal detection in case of a gross malfunction of an AP infrastructure. The system without proposed improvements may give the errors greater than 30 percent and 7 metres respectively in case of not detected changes in the AP’s infrastructure.
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References
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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). http://dx.doi.org/10.1007/978-3-642-38577-3_63
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, Part I. CCIS, vol. 383, pp. 122–131. Springer, Heidelberg (2013). http://dx.doi.org/10.1007/978-3-642-41013-0_13
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 2013
Papapostolou, A., Chaouchi, H.: Scene analysis indoor positioning enhancements. Annales des Télécommunications 66, 519–533 (2011)
Roos, T., Myllymaki, P., Tirri, H., Misikangas, P., Sievanen, J.: A probabilistic approach to wlan user location estimation. International Journal of Wireless Information Networks 9(3), 155–164 (2002)
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). http://www.mdpi.com/1424-8220/15/4/7096
Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J., Gao, X.G.: A wireless lan-based indoor positioning technology. IBM Journal of Research and Development 48(5–6), 617–626 (2004)
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Górak, R., Luckner, M. (2015). Malfunction Immune Wi–Fi Localisation Method. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_31
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DOI: https://doi.org/10.1007/978-3-319-24069-5_31
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