Abstract
This paper presents the maximum likelihood (ML)-based approaches for relative location estimation over the correlated wireless sensor networks (WSNs), which innovatively exploit the principal component analysis (PCA) and probabilistic PCA to transform all received signal strength (RSS) measurements into useful information. Simulation results reveal that the proposed approaches remarkably outperform the other existing schemes when a high correlation exists or a strong noise power occurs. Taking the path-loss exponent into consideration, it is observed that the higher the path-loss exponent, the lower the location estimation error. These results show that the proposed approach is suitable for the practical correlated wireless channels.
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References
Sendonaris, A., Erkipand, E., Aazhang, B.: User cooperation diversity—Part I: System description. IEEE Trans. Commun. 51(11), 1927–1938 (2003)
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst., Man, Cybern. C, Appl. Rev. 37(6), 1067–1080 (2007)
Vossiek, M., Wiebking, L., Gulden, P., Wieghardt, J., Hoffmann, C., Heide, P.: Wireless local positioning. IEEE Microw. Mag. 4(4), 77–86 (2003)
Al-Jazzar, S., Ghogho, M.: A joint TOA/AOA constrained minimization method for locating wireless devices in non-line-of-sight environment. In: Proc. IEEE VTC-Fall (2007)
Al-Jazzar, S., Caffery, J.J.: ML and Bayesian TOA location estimators for NLOS environments. In: Proc. IEEE VTC-Fall (2002)
Patwari, N., Hero, A.O., Perkins, M., Correaland, N.S., O’Dea, R.J.: Relative location estimation in wireless sensor networks. IEEETrans. Signal Processing 51(8), 2137–2148 (2003)
Li, Z., Trappe, W., Zhang, Y., Nath, B.: Robust statistical methods for securing wireless localization. In: Proc. Symposium on Information Processing in Sensor Networks (2005)
Chang, C.-H., Liao, W.: Revisiting relative location estimation in wireless sensor networks. In: Proc. IEEE ICC (2009)
Chang, C.-H., Liao, W.: A probabilistic model for relative location estimation in wireless sensor networks. IEEE Commun. Lett. 13(12), 893–895 (2009)
Chris, S., Jan, M.R., Koen, L.: Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In: Proc. USENIX Annual Technical Conference (2002)
Fang, S., Lin, T.: Principal component localization in indoor WLAN environments. IEEE Trans. Mobile Comput. (published online)
Fang, S., Wang, C.-H.: A dynamic hybrid projection approach for improved Wi-Fi location fingerprinting. IEEE Trans. Veh. Technol. 60(3), 1037–1044 (2011)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3), 611–622
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiely& Sons (2000)
Levenberg, K.: A method for the solution of certain non-linear problems in least squares. The Quarterly of Applied Mathematics 2, 164–168 (1944)
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Chu, SI., Lien, CY., Lin, WC., Huang, YJ., Pan, CL., Chen, PY. (2014). Relative Location Estimation over Wireless Sensor Networks with Principal Component Analysis Technique. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_23
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DOI: https://doi.org/10.1007/978-3-319-01796-9_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
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