Abstract
Considering the low localization accuracy caused by uncertainty of indoor environment changes and many unexpected factors like man-made interference, this paper presents an indoor space localization method based on Hidden Markov Model. By adding indoor localization component program to the system of off-the-shelf WSN deployment, this method can collect mobile-node’s RF characteristic parameters relation to the indoor positioning , without changing the network topology and system function. Then these parameters are processed by Hidden Markov Model to eliminate the effect of indoor environment changes and man-made interference, consequently getting mobile-node’s precise localization in the room.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Torres-Solis, J., Falk, T.H., et al.: A review of indoor localization technologies: towards navigational assistance for topographical disorientation. Ambient Intelligence, 51–84 (2010)
Rohrig, C., Muller, M.: Indoor location tracking in non-line-of-sight environments using a IEEE 802.15. 4a wireless network. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009. IEEE (2009)
Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of the IEEE Infocom, Israel (2000)
Zhou, G., He, T., Krishnamurthy, S., Stankovic, J.A.: Impact of Radio Irregularity on Wireless Sensor Networks. In: Proceedings of MobiSys 2004, Boston, MA (2004)
Elnahraway, E., Li, X., Martin, R.P.: The limits of localization using RSS. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, MD, USA (2004)
Nagpal, R., Shrobe, H., Bachrach, J.: Organizing a global coordinate system from local information on an ad hoc sensor network. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 333–348. Springer, Heidelberg (2003)
Kjærgaard, M.B.: A taxonomy for radio location fingerprinting. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 139–156. Springer, Heidelberg (2007)
Mitilineos, S.A., Kyriazanos, D.M., Segou, O.E.: Indoor Localization with Wireless Sensor Networks. Progress in Electromagnetics Research 109, 441–474 (2010)
Indoor Localization System using Wireless Sensor Networks for Stationary and Moving Target (2009)
Welch, G., Bishop, G.: An introduction to the Kalman filter (1995)
Paul, A.S., Wan, E.A.: RSSI-based indoor localization and tracking using sigma-point kalman smoothers. IEEE Journal of Selected Topics in Signal Processing 3(5), 860–873 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, X., Chen, Y., Gui, Q., Xiong, C. (2014). Application and Realization of Indoor Localization Based on Hidden Markov Model. In: Sun, L., Ma, H., Hong, F. (eds) Advances in Wireless Sensor Networks. CWSN 2013. Communications in Computer and Information Science, vol 418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54522-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-54522-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54521-4
Online ISBN: 978-3-642-54522-1
eBook Packages: Computer ScienceComputer Science (R0)
