Abstract
With the increasing popularity of smart phones, knowing the accurate position of users has become critical to many context-aware applications. In this paper, we introduce a novel Probabilistic Infrastructureless Navigation (ProbIN) system for GPS-challenging environments. ProbIN uses inertial and magnetic sensors in mobile phones to derive users’ current location. Instead of relying on basic laws of physics (e.g. double integral of acceleration equals to displacement) ProbIN uses a statistical model for estimating the position of users. This statistical model is built based on the user’s data by applying machine learning techniques from the statistical machine translation field. Thus, ProbIN can capture the user’s specific walking patterns and is, therefore, more robust against noisy sensor readings. In the evaluation of our approach we focused on the most common daily scenarios. We conducted experiments with a user walking and carrying the phone in different settings such as in the hand or in the pocket. The results of the experiments show that even though the mobile phone was not mounted to the user’s body, ProbIN outperforms the state-of-the-art dead reckoning approaches.
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References
Beauregard, S.: Infrastructureless pedestrian positioning. PhD thesis, University of Bremen (2009)
Beauregard, S., Haas, H.: Pedestrian dead reckoning: A basis for personal positioning. In: Proceedings of the 3rd Workshop on Positioning, Navigation and Communication (WPNC 2006), vol. 06, pp. 27–35 (2006)
Beauregard, S.: Omnidirectional pedestrian navigation for first responders. In: Workshop on Positioning, Navigation and Communication, pp. 33–36. IEEE (March 2007)
Brown, P.F., Cocke, J., Pietra, S.A.D., Pietra, V.J.D., Jelinek, F., Lafferty, J.D., Mercer, R.L., Roossin, P.S.: A statistical approach to machine translation. Computational Linguistics 16(2), 79–85 (1990)
Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19(2), 263–311 (1993)
Collin, J., Mezentsev, O., Lachapelle, G.: Indoor positioning system using accelerometry and high accuracy heading sensors. In: Proceedings of the 16th International Technical Meeting of the Satellite Division of the Institute of Navigation ION GPS/GNSS 2003, pp. 1–7 (2003)
Dempster, A.P., Laird, N.M., Rubin, D.B., et al.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)
Feliz, R., Zalama, E., García-Bermejo, J.G.: Pedestrian tracking using inertial sensors. Journal of Physical Agents 3(1), 35–42 (2009)
Fischer, C., Muthukrishnan, K., Hazas, M., Gellersen, H.: Ultrasound-aided pedestrian dead reckoning for indoor navigation. In: Proceedings of the First ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT 2008), vol. 31 (2008)
Germann, U., Jahr, M., Knight, K., Marcu, D., Yamada, K.: Fast decoding and optimal decoding for machine translation. In: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, vol. 39, pp. 228–235. Association for Computational Linguistics (2001)
Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis, 9th edn. WileyBlackwell (March 2005)
Lin, H., Zhang, Y., Griss, M., Landa, I.: WASP: an enhanced indoor locationing algorithm for a congested Wi-Fi environment. In: Proceedings of the Workshop on Mobile Entity Localization and Tracking in GPS-less Environnments, pp. 183–196 (2009)
Nguyen, T.-L., Zhang, Y., Griss, M.: Probin: Probabilistic inertial navigation. In: 2010 IEEE 7th International Conference on Mobile Adhoc and Sensor Systems (MASS), pp. 650 –657 (November 2010)
Ojeda, L., Borenstein, J.: Non-GPS navigation for security personnel and first responders. The Journal of Navigation 60(3), 391–407 (2007)
Randell, C., Djiallis, C., Muller, H.: Personal position measurement using dead reckoning. In: Proceedings of Seventh IEEE International Symposium on Wearable Computers, pp. 166–173 (2003)
Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1), 55 (2001)
Vogel, S., Zhang, Y., Huang, F., Tribble, A., Venugopal, A., Zhao, B., Waibel, A.: The CMU statistical machine translation system. In: Proceedings of MT Summit, vol. 9 (2003)
Welch, G., Bishop, G.: An introduction to the Kalman filter, pp. 1–16. University of North Carolina at Chapel Hill, Chapel (1995)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Nguyen, L.T., Zhang, Y. (2012). Probabilistic Infrastructureless Positioning in the Pocket. In: Zhang, J.Y., Wilkiewicz, J., Nahapetian, A. (eds) Mobile Computing, Applications, and Services. MobiCASE 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32320-1_20
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DOI: https://doi.org/10.1007/978-3-642-32320-1_20
Publisher Name: Springer, Berlin, Heidelberg
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