Abstract
Faulty signals from Global Navigation Satellite Systems (GNSS) 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 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 on two separate real-world datasets and proved to accurately detect and exclude GNSS faults in less computational time than traditional residual-based or solution separation FDE methods.
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