Detection and Exclusion of Multiple Faults using Euclidean Distance Matrices

Derek Knowles, Grace Gao

Abstract:	Numerous methods have been proposed for global navigation satellite system (GNSS) receivers to detect faulty GNSS signals. One such fault detection and exclusion (FDE) method is based on the mathematical concept of Euclidean distance matrices (EDMs). This paper outlines a greedy approach that uses an improved Euclidean distance matrix-based fault detection and exclusion algorithm. The novel greedy EDM FDE method implements a new fault detection test statistic and fault exclusion strategy that drastically simplifies the complexity of the algorithm over previous work. To validate the novel greedy EDM FDE algorithm, we created a simulated dataset using receiver locations from around the globe. The simulated dataset allows us to verify our results on 2,601 different satellite geometries. The Python implementation of the greedy EDM FDE algorithm is shown to be computed much more rapidly than a comparable greedy residual FDE method while obtaining similar fault exclusion accuracy. We provide discussion on the comparative time complexities of greedy EDM FDE and greedy residual FDE. We also explain common modifications to greedy residual FDE that can also be added to greedy EDM FDE to alter performance characteristics.
Published in:	Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023) September 11 - 15, 2023 Hyatt Regency Denver Denver, Colorado
Pages:	1774 - 1782
Cite this article:	Knowles, Derek, Gao, Grace, "Detection and Exclusion of Multiple Faults using Euclidean Distance Matrices," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 1774-1782. https://doi.org/10.33012/2023.19280
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