Abstract
In this paper, we present an investigation of indoor objects positioning using the received Wi-Fi signal strength in the realistic environment with the presence of obstacles. Wi-Fi RSS based positioning is a promising alternative to other techniques for locating indoor objects. Two factors may lead to the low Wi-Fi RSS positioning accuracy: the existence of moving obstacles, and the limited number of available anchor nodes. We propose a novel approach to locating a target object in a given area by introducing a hidden factor for a reduced form of probabilistic estimator. This estimator is unbiased with the scalability in field size. With the selection of a Gaussian prior on this hidden factor characterizing the effects of RSS drop introduced by obstacles, we convert the positioning prediction into a maximum a posteriori problem, then apply expectation-maximization algorithm and conjugate gradient optimization to find the solution. Simulations in various settings show that the proposed approach presents better performance compared to other state-of-the-art RSS range-based positioning algorithms.
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References
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 1067–1080 (2007)
Weng, Y., Xiao, W., Xie, L.: Total least squares method for robust source localization in sensor networks using TDOA measurements. International Journal of Distributed Sensor Networks (2011)
Mao, G., Fidan, B., Anderson, B.D.O.: Wireless sensor network localization techniques. Computer Networks. The International Journal of Computer and Telecommunications Networking, 2529–2553 (2007)
Swangmuang, N., Krishnamurthy, P.: Location Fingerprint Analyses Toward Efficient Indoor Positioning. In: Sixth Annual IEEE International Conference on Pervasive Computing and Communications, pp. 101–109 (2008)
Ladd, A.M., Bekris, K.E., Rudys, A.P., Wallach, D.S., Kavraki, L.E.: On the feasibility of using wireless Ethernet for indoor localization. Name IEEE Trans. Robotics and Automation, 555–559 (2004)
Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J., Gao, X.: A wireless LAN-based in-door positioning technology. IBM J. Res. & Dev. (2004)
Wang, J., Urriza, P., Han, Y., Cabric, D.: Weighted centroid algorithm for estimating primary user location: Theoretical analysis and distributed implementation. IEEE Transactions on Wireless Communications (June 2011)
Chen, H., Sezaki, K., Deng, P., So, H.C.: An improved DV-hop localization algorithm for wireless sensor networks. In: Proc. IEEE Conference on Industrial Electronics and Applications (ICIEA 2008), Singapore, pp. 1557–1561 (2008)
Kumar, P., Reddy, L., Varma, S.: Distance measurement and error estimation scheme for RSSI based localization in Wireless Sensor Networks. In: Fifth IEEE Conference on Wireless Communication and Sensor Networks (WCSN), pp. 1–4 (2009)
Ahn, H.-S., Yu, W.: Indoor localization techniques based on wireless sensor networks. Mobile Robots State of the Art in Land, Sea, Air, and Collaborative Missions, 277–302 (2009)
Ali-Rantala, P., Ukkonen, L., Sydanheimo, L., Keskilammi, M., Kivikoski, M.: Different kinds of walls and their effect on the attenuation of radiowaves indoors. In: IEEE APS International Symposium, Columbus, OH, USA, pp. 1020–1023 (2003)
Chen, R.C., Lin, Y.C.: An Indoor Location Identification System Based on Neural Network and Genetic Algorithm. In: 3rd International Conferece on Awareness Science and Technology (iCAST), pp. 27–30 (2011)
Yang, Z., Wu, C., Liu, Y.: Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (MobiCom 2012), pp. 269–280 (2012)
Arya, A.: An analysis of radio fingerprints behavior in the context of RSS-based boction fingerprinting systems. In: 2011 IEEE 22nd International Symposium on Date of Conference on Personal Indoor and Mobile Radio Communications (PIMRC), pp. 536–540 (2011)
Laaraiedh, M., Avrillon, S., Uguen, B.: Enhancing positioning accuracy through RSS based ranging and weighted least square approximation. In: Proceedings of POCA Conference, Antwerp, Belgium (2009)
Costa, J., Patwari, N., Hero III, A.O.: Distributed multidimensional scaling with adaptive weighting for node localization in sensor networks. ACM Trans. Sensor Netw. (2005)
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Shen, G., Xie, Z. (2013). Wi-Fi RSS Based Indoor Positioning Using a Probabilistic Reduced Estimator. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds) Active Media Technology. AMT 2013. Lecture Notes in Computer Science, vol 8210. Springer, Cham. https://doi.org/10.1007/978-3-319-02750-0_5
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DOI: https://doi.org/10.1007/978-3-319-02750-0_5
Publisher Name: Springer, Cham
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