A fault detection method based on orthogonality for integrated navigation system

Jing Yu; Pengpeng Li; Zheng Wei; Xiaohui Liu; Weihua Mou

doi:10.1117/12.3052377

30 December 2024 A fault detection method based on orthogonality for integrated navigation system

Jing Yu, Pengpeng Li, Zheng Wei, Xiaohui Liu, Weihua Mou

Author Affiliations +

Proceedings Volume 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024); 133940S (2024) https://doi.org/10.1117/12.3052377
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 2024, Hohhot, China

Abstract

Improving the reliability of integrated navigation systems requires effective fault detection and isolation strategies. In response to false alarms in jump soft fault detection grounded on the orthogonality principle, this study proposes refinements to the approach for data selection within a sliding window. The refined method introduces an improved orthogonality fault detection algorithm, which enhances detection accuracy and reduces the likelihood of false alarms. By harnessing data within the sliding window optimally, the algorithm computes the orthogonal average as the test statistic without necessitating an increase in the sliding window length. Furthermore, by combining the traditional residual chi-square test and extrapolation chi-square test, more precise judgments can be made regarding fault type and occurrence time. Ultimately, through the integration of traditional residual chi-square and residual extrapolation chi-square detection, this methodology enhances fault classification capabilities, reduces false alarm rates, and enables the effective identification of pulse, abrupt, and gradual faults even in scenarios with limited fault signal strength.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jing Yu, Pengpeng Li, Zheng Wei, Xiaohui Liu, and Weihua Mou "A fault detection method based on orthogonality for integrated navigation system", Proc. SPIE 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 133940S (30 December 2024); https://doi.org/10.1117/12.3052377

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KEYWORDS

Detection and tracking algorithms

Navigation systems

Windows

Covariance matrices

Simulations

Signal filtering

Covariance

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