Fiber Optic Gyro Random Error Suppression Based on Dual Adaptive Kalman Filter
Tallennettuna:
| Julkaisussa: | Micromachines vol. 16, no. 8 (2025), p. 884-898 |
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| Päätekijä: | |
| Muut tekijät: | , , , , |
| Julkaistu: |
MDPI AG
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| Aiheet: | |
| Linkit: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3244047378 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-666X | ||
| 024 | 7 | |a 10.3390/mi16080884 |2 doi | |
| 035 | |a 3244047378 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231537 |2 nlm | ||
| 100 | 1 | |a Li, Hongcai |u Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi’an 710025, China | |
| 245 | 1 | |a Fiber Optic Gyro Random Error Suppression Based on Dual Adaptive Kalman Filter | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Fiber optic gyroscopes | ||
| 653 | |a Random errors | ||
| 653 | |a Algorithms | ||
| 653 | |a Methods | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Kalman filters | ||
| 653 | |a Real time | ||
| 653 | |a Variance | ||
| 653 | |a Random walk | ||
| 700 | 1 | |a Liang Zhe |u Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi’an 710025, China | |
| 700 | 1 | |a Zhou Zhaofa |u Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi’an 710025, China | |
| 700 | 1 | |a Zhang, Zhili |u Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi’an 710025, China | |
| 700 | 1 | |a Zhao Junyang |u Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi’an 710025, China | |
| 700 | 1 | |a Tian Longjie |u Institute of Optics and Electronics, School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China | |
| 773 | 0 | |t Micromachines |g vol. 16, no. 8 (2025), p. 884-898 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244047378/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244047378/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244047378/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |