Jackknife Test for Faulty GNSS Measurements Detection Under Non-Gaussian Noises

Penggao Yan

Peer Reviewed

Abstract: Fault detection is crucial to ensure the reliability of navigation systems. However, mainstream fault detection methods are developed based on Gaussian noise assumptions, while other methods targeting non-Gaussian noises lack rigorous statistical properties. The performance and reliability of these methods are challenged in real-world applications. This paper proposes a fault detection method for linearized pseudorange-based positioning systems under non-Gaussian noises. Specifically, this paper proposes a test statistic based on the jackknife technique, which is proved to be the linear combination of measurement noises without any assumption about noise distribution. Furthermore, a hypothesis test with the Bonferroni correction is constructed to detect potential faults in measurements. In a worldwide simulation, the proposed method demonstrates superior performance than the multiple hypothesis solution separation (MHSS) method under non-Gaussian noises. The reliability of the proposed method is further examined in detecting artificially injected faults for a differential global navigation satellite system (DGNSS) positioning system. Moreover, a real-world application to detect satellite clock anomalies for a single point positioning (SPP) system is investigated. The results show a significant improvement in reducing detection delay (8 minutes earlier than MHSS).
Published in: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024)
September 16 - 20, 2024
Hilton Baltimore Inner Harbor
Baltimore, Maryland
Pages: 1619 - 1641
Cite this article: Yan, Penggao, "Jackknife Test for Faulty GNSS Measurements Detection Under Non-Gaussian Noises," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 1619-1641. https://doi.org/10.33012/2024.19837
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