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Sensors 2018, 18(7), 2186; https://doi.org/10.3390/s18072186

An Outlier Detection Method Based on Mahalanobis Distance for Source Localization

1
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2
KU Leuven, ESAT-DRAMCO, Ghent Technology Campus, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Received: 20 June 2018 / Revised: 4 July 2018 / Accepted: 5 July 2018 / Published: 7 July 2018
(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)
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Abstract

This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtained by the proposed division and greedy replacement method. The Mahalanobis distance based on robust mean and covariance matrix estimation method is then introduced to identify the outliers from the position sets. Finally, the weighted least squares method based on the reliable probabilities and distances is proposed for source localization. The simulation and experimental results show that the proposed method outperforms representative methods when unreliable AOAs are present. View Full-Text
Keywords: angle of arrival; source localization; outlier detection; Mahalanobis distance; unreliable nodes angle of arrival; source localization; outlier detection; Mahalanobis distance; unreliable nodes
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yan, Q.; Chen, J.; De Strycker, L. An Outlier Detection Method Based on Mahalanobis Distance for Source Localization. Sensors 2018, 18, 2186.

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