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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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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