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Sensors 2008, 8(11), 7344-7358; doi:10.3390/s8117344

Evaluation of Different Outlier Detection Methods for GPS Networks

Department of Geodesy and Photogrammetry, Karadeniz Technical University, Trabzon, 61080 Turkey
Department of Geodesy and Photogrammetry, Hacettepe University, Ankara, Turkey
Author to whom correspondence should be addressed.
Received: 4 November 2008 / Revised: 14 November 2008 / Accepted: 17 November 2008 / Published: 17 November 2008
(This article belongs to the Section Remote Sensors)
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GPS (Global Positioning System) devices can be used in many applications which require accurate point positioning in geosciences. Accuracy of GPS decreases due to outliers resulted from the errors inherent in GPS observations. Several approaches have been developed to detect outliers in geodetic observations. It is important to determine which method is most effective at distinguishing outliers from normal observations. This paper investigates the behavior of conventional statistical test methods (Data Snooping (DS), Tau and t tests), some robust methods (Andrews’s M-Estimation, Huber’s MEstimation, Tukey’s M-Estimation, Danish Method, Yang-I M-Estimation, Yang-II MEstimation, and fuzzy logic method in detection of outliers for three GPS networks having different characteristics. Test results are evaluated and the performances of different methods are presented quantitatively. View Full-Text
Keywords: Robust estimation; Fuzzy logic; GPS; Statistical test; Data Snooping; Membership value Robust estimation; Fuzzy logic; GPS; Statistical test; Data Snooping; Membership value

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Gökalp, E.; Güngör, O.; Boz, Y. Evaluation of Different Outlier Detection Methods for GPS Networks. Sensors 2008, 8, 7344-7358.

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