Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation
In this paper, a robust central difference Kalman filter is proposed to address the process uncertainty and non-Gaussian measurement noise induced by the vehicle’s severe maneuver and abnormal measurements in MEMS-SINS/GNSS integrated navigation system. Compared with the state-of-the-art...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9441009/ |
| _version_ | 1818574048572997632 |
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| author | Kaiqiang Feng Jie Li Debiao Zhang Xiaokai Wei Jianping Yin |
| author_facet | Kaiqiang Feng Jie Li Debiao Zhang Xiaokai Wei Jianping Yin |
| author_sort | Kaiqiang Feng |
| collection | DOAJ |
| description | In this paper, a robust central difference Kalman filter is proposed to address the process uncertainty and non-Gaussian measurement noise induced by the vehicle’s severe maneuver and abnormal measurements in MEMS-SINS/GNSS integrated navigation system. Compared with the state-of-the-art noise distribution based robust filter, in the proposed filter, the process uncertainty and measurement uncertainty are simultaneously suppressed based on a new constructed cost function, which is independent of noise distribution and more insensitive to the non-Gaussian noise. To be specific, the statistical linearization approach is first presented to derive a linear-like regression model. Then, by resorting to the innovation orthogonal theory and Cholesky triangular decomposition, the fading factor of cost function is adaptively and robustly determined in the process of iteration, where the filtering performance and the stability of the algorithm under the condition of process uncertainty are extremely enhanced. Furthermore, the correntropy using the mixture of two Gaussian functions as the kernel function is incorporated into the cost function to prevent the non-Gaussian measurement noise. Our extensive simulation and car-mounted experiment demonstrate that the proposed filter can achieve higher estimation accuracy and better robustness as compared with the related state-of-the-art methods. |
| first_indexed | 2024-12-15T00:21:15Z |
| format | Article |
| id | doaj.art-a0d8be08d49a4621894430e3dc7109e3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-15T00:21:15Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a0d8be08d49a4621894430e3dc7109e32022-12-21T22:42:18ZengIEEEIEEE Access2169-35362021-01-019807728078610.1109/ACCESS.2021.30839639441009Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated NavigationKaiqiang Feng0https://orcid.org/0000-0002-8080-6025Jie Li1https://orcid.org/0000-0002-2533-5849Debiao Zhang2https://orcid.org/0000-0002-2385-993XXiaokai Wei3https://orcid.org/0000-0002-5455-0521Jianping Yin4https://orcid.org/0000-0002-7276-0653Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education, North University of China, Taiyuan, ChinaNational Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, ChinaKey Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education, North University of China, Taiyuan, ChinaKey Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education, North University of China, Taiyuan, ChinaCollege of Mechatronic Engineering, North University of China, Taiyuan, ChinaIn this paper, a robust central difference Kalman filter is proposed to address the process uncertainty and non-Gaussian measurement noise induced by the vehicle’s severe maneuver and abnormal measurements in MEMS-SINS/GNSS integrated navigation system. Compared with the state-of-the-art noise distribution based robust filter, in the proposed filter, the process uncertainty and measurement uncertainty are simultaneously suppressed based on a new constructed cost function, which is independent of noise distribution and more insensitive to the non-Gaussian noise. To be specific, the statistical linearization approach is first presented to derive a linear-like regression model. Then, by resorting to the innovation orthogonal theory and Cholesky triangular decomposition, the fading factor of cost function is adaptively and robustly determined in the process of iteration, where the filtering performance and the stability of the algorithm under the condition of process uncertainty are extremely enhanced. Furthermore, the correntropy using the mixture of two Gaussian functions as the kernel function is incorporated into the cost function to prevent the non-Gaussian measurement noise. Our extensive simulation and car-mounted experiment demonstrate that the proposed filter can achieve higher estimation accuracy and better robustness as compared with the related state-of-the-art methods.https://ieeexplore.ieee.org/document/9441009/MEMS-SINS/GNSS integrated navigationrobust central difference Kalman filtermixture correntropy |
| spellingShingle | Kaiqiang Feng Jie Li Debiao Zhang Xiaokai Wei Jianping Yin Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation IEEE Access MEMS-SINS/GNSS integrated navigation robust central difference Kalman filter mixture correntropy |
| title | Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation |
| title_full | Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation |
| title_fullStr | Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation |
| title_full_unstemmed | Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation |
| title_short | Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation |
| title_sort | robust central difference kalman filter with mixture correntropy a case study for integrated navigation |
| topic | MEMS-SINS/GNSS integrated navigation robust central difference Kalman filter mixture correntropy |
| url | https://ieeexplore.ieee.org/document/9441009/ |
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