Euclidean Distance Matrix-based Rapid Fault Detection and Exclusion

Derek Knowles and Grace Gao

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.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 3382 - 3391
Cite this article: Knowles, Derek, Gao, Grace, "Euclidean Distance Matrix-based Rapid Fault Detection and Exclusion," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 3382-3391.
https://doi.org/10.33012/2021.17973
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In