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Fiber Optic Gyro Random Error Suppression Based on Dual Adaptive Kalman Filter

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Publicado en:Micromachines vol. 16, no. 8 (2025), p. 884-898
Autor principal: Li, Hongcai
Otros Autores: Liang Zhe, Zhou Zhaofa, Zhang, Zhili, Zhao Junyang, Tian Longjie
Publicado:
MDPI AG
Materias:
Fiber optic gyroscopes
Random errors
Algorithms
Methods
Wavelet transforms
Kalman filters
Real time
Variance
Random walk
Acceso en línea:Citation/Abstract
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Resumen:The random error of fiber optic gyros is a critical factor affecting their measurement accuracy. However, the statistical characteristics of these errors exhibit time-varying properties, which degrade model fidelity and consequently impair the performance of random error suppression algorithms. To address these issues, this study first proposes a recursive dynamic Allan variance calculation method that effectively mitigates the poor real-time performance and spectral leakage inherent in conventional dynamic Allan variance techniques. Subsequently, the recursive dynamic Allan variance is integrated with the process variance estimation of Kalman filtering to construct a dual-adaptive Kalman filter capable of autonomously switching and adjusting between model parameters and noise variance. Finally, both static and dynamic validation experiments were conducted to evaluate the proposed method. The experimental results demonstrate that, compared to existing algorithms, the proposed approach significantly enhances the suppression of angular random walk errors in fiber optic gyros.
ISSN:2072-666X
DOI:10.3390/mi16080884
Fuente:Engineering Database