Abstract
Because WLAN signal strength data is vulnerable to external interference and its validity period is short-lived, it is necessary to reconstruct radio map to improve positioning performance. We add the weight data to the original fingerprint library, which is obtained by the reliability of information. Using it, we can know the importance of neighborhood points selected in the online phase and get better positioning performance. In the positioning phase, the KD tree is added to improve the positioning efficiency of the positioning algorithm. Finally, the positioning accuracy and efficiency can be improved.
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Gartner, G., Ortag, F.: Advances in location-based services. Lect. Notes Geoinformation Cartogr. 5(8), 97–106 (2014)
Feng, C., Au, W.S.A., Valaee, S., et al.: Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans. Mob. Comput. 11(12), 1983–1993 (2012)
Baala, O., Zheng, Y., Caminada, A.: The impact of AP placement in WLAN-based indoor positioning system. In: Eighth International Conference on Networks, pp. 12–17. IEEE Computer Society (2009)
Pan, J.J., Pan, S.J., Yin, J., et al.: Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 587–600 (2012)
Au, A.W.S., Feng, C., Valaee, S., et al.: Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device. IEEE Trans. Mob. Comput. 12(10), 2050–2062 (2013)
Bong, W., Kim, Y.C.: Reconstruction of radio map from sparse RSS data by discontinuity preserving smoothing. In: ACM Research in Applied Computation Symposium, pp. 227–231. ACM (2012)
Li, X., Deng, Z.: Radio map reconstruction technology in indoor fingerprint localization algorithm (2012)
Deng, Z., Ma, L., Xu, Y.: Intelligent AP selection for indoor positioning in wireless local area network. In: International ICST Conference on Communications and Networking in China, pp. 257–261. IEEE Computer Society (2011)
Umair, M.Y., Xiao, D., Li, A., et al.: Access point selection for indoor positioning in a WLAN environment using an algorithm based on RSSI and dilution of precision. Environ. Entomol. 26(3), 91–99 (2014)
Yang, L., Chen, H., Cui, Q., et al.: Probabilistic-KNN: a novel algorithm for passive indoor-localization scenario. In: Vehicular Technology Conference, pp. 1–5. IEEE (2015)
Chen, X.K., Liu, Z.S.: K nearest neighbor query based on improved Kd-tree construction algorithm. J. Guangdong Univ. Technol. 31, 119–123 (2014)
Kaemarungsi, K., Krishnamurthy, P.: Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Elsevier Science Publishers B. V. (2012)
Lee, M., Han, D.: Voronoi tessellation based interpolation method for Wi-Fi radio map construction. IEEE Commun. Lett. 16(3), 404–407 (2012)
Abubaker, M., Ashour, W.: Efficient data clustering algorithms: improvements over kmeans. Int. J. Intell. Syst. Appl. 5(3), 37–49 (2013)
Wang, L., Wong, W.C.: A RSS based statistical localization algorithm in WLAN. In: International Conference on Signal Processing and Communication Systems, pp. 1–5 (2012)
Acknowledgment
This paper is supported by National Natural Science Foundation of China (61571162), Ministry of Education - China Mobile Research Foundation (MCM20170106) and Heilongjiang Province Natural Science Foundation (F2016019).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ma, L., Li, J., Xu, Y. (2018). Weight Matrix Analysis Algorithm for WLAN Indoor Positioning System. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_32
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DOI: https://doi.org/10.1007/978-3-030-00557-3_32
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