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Sensors 2019, 19(8), 1836; https://doi.org/10.3390/s19081836

Specific Direction-Based Outlier Detection Approach for GNSS Vector Networks

College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China
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Author to whom correspondence should be addressed.
Received: 20 March 2019 / Revised: 15 April 2019 / Accepted: 16 April 2019 / Published: 17 April 2019
(This article belongs to the Collection Positioning and Navigation)
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Abstract

In this paper we propose an outlier detection approach for GNSS vector networks based on the specific direction (i.e., SD approach), along which the test statistic constructed reaches the maximum. We derive the unit vector of this specific direction in detail, and prove that the unit vector is the same as that determined by the outlier estimates in three-dimensional (3D) approach, while the distribution of the maximum test statistic in this direction is the square root of Chi-squared distribution. Therefore, eliminating an outlier along this specific direction can get the same result as that of eliminating all three components of outlier vector in 3D approach. The mathematical equivalence of SD approach and 3D approach is further demonstrated by a real GNSS network. Moreover, preliminary application of the SD approach to detect the abnormal antenna height measurement is carried out in terms of numerical simulations of multiple baseline solutions, and it shows that the SD approach can effectively detect baselines that are directly infected by corresponding receiver antenna height errors. View Full-Text
Keywords: outlier detection; GNSS networks; baseline vector; antenna height outlier detection; GNSS networks; baseline vector; antenna height
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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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Nie, Y.; Yang, L.; Shen, Y. Specific Direction-Based Outlier Detection Approach for GNSS Vector Networks. Sensors 2019, 19, 1836.

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