Faulty signals from global navigation satellite systems (GNSSs) often lead to erroneous position estimates. A variety of fault detection and exclusion (FDE) methods have been proposed in prior research to both detect and exclude faulty measurements. This paper introduces a new technique for the FDE of GNSS measurements using Euclidean distance matrices. After a brief introduction to Euclidean distance matrices, both the detection and exclusion strategy is explained in detail. Euclidean distance matrix-based FDE is verified in two separate real-world data sets and proven to accurately detect and exclude GNSS faults on an average of 1.4-times faster than residual-based FDE and 70-times faster than solution separation FDE.


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    Title :

    Euclidean Distance Matrix-Based Rapid Fault Detection and Exclusion


    Contributors:
    Derek Knowles (author) / Grace Gao (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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