Abstract
<h3>Abstract</h3> 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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