A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation
The classical Kalman-based filtering algorithm, such as extended Kalman filter or unscented Kalman filter, commonly assumes that the accurate system model or precise noise statistics is known for using. Hence, these filters are not robust estimation to practical systems and always with poor stabilit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8935212/ |
| _version_ | 1818663848265121792 |
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| author | Juan Xia Shesheng Gao Yongmin Zhong Jiahao Zhang Chengfan Gu Yang Liu |
| author_facet | Juan Xia Shesheng Gao Yongmin Zhong Jiahao Zhang Chengfan Gu Yang Liu |
| author_sort | Juan Xia |
| collection | DOAJ |
| description | The classical Kalman-based filtering algorithm, such as extended Kalman filter or unscented Kalman filter, commonly assumes that the accurate system model or precise noise statistics is known for using. Hence, these filters are not robust estimation to practical systems and always with poor stability, low precision or even divergence since uncertain items exist. In order to tackle these issues, a novel scheme referred to as the fitting H-infinity Kalman filter (FHKF) is proposed and used for robust estimation of the nonlinear uncertain systems. The hardcore of FHKF is the fitting transformation, which is a numerical approximation approach to get the estimation values of coefficient matrix based on least weighted squares. Moreover, FHKF is proposed by applying the coefficient matrix to the structure of the extended H-infinity Kalman filter. Based on the stochastic stability lemma, the stability analysis is presented to verify the error boundness of the proposed filtering. Its efficiency is demonstrated by taking Monte Carlo simulation for the uncertain system and practical experiments in the INS/GPS integrated navigation. |
| first_indexed | 2024-12-17T05:23:22Z |
| format | Article |
| id | doaj.art-dc66e51b25064033b789378bb4d7ab5a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:23:22Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dc66e51b25064033b789378bb4d7ab5a2022-12-21T22:01:57ZengIEEEIEEE Access2169-35362020-01-018105541056810.1109/ACCESS.2019.29604108935212A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting TransformationJuan Xia0https://orcid.org/0000-0001-8391-5262Shesheng Gao1https://orcid.org/0000-0002-7980-9085Yongmin Zhong2https://orcid.org/0000-0002-0105-9296Jiahao Zhang3https://orcid.org/0000-0001-9421-2415Chengfan Gu4https://orcid.org/0000-0002-3510-0162Yang Liu5https://orcid.org/0000-0002-4308-2195Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaSchool of Automatics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Engineering, RMIT University, Bundoora, VIC, AustraliaSchool of Automatics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Engineering, RMIT University, Bundoora, VIC, AustraliaChina JIKAN Research Institute of Engineering Investigations and Design, Company, Ltd., Xi’an, ChinaThe classical Kalman-based filtering algorithm, such as extended Kalman filter or unscented Kalman filter, commonly assumes that the accurate system model or precise noise statistics is known for using. Hence, these filters are not robust estimation to practical systems and always with poor stability, low precision or even divergence since uncertain items exist. In order to tackle these issues, a novel scheme referred to as the fitting H-infinity Kalman filter (FHKF) is proposed and used for robust estimation of the nonlinear uncertain systems. The hardcore of FHKF is the fitting transformation, which is a numerical approximation approach to get the estimation values of coefficient matrix based on least weighted squares. Moreover, FHKF is proposed by applying the coefficient matrix to the structure of the extended H-infinity Kalman filter. Based on the stochastic stability lemma, the stability analysis is presented to verify the error boundness of the proposed filtering. Its efficiency is demonstrated by taking Monte Carlo simulation for the uncertain system and practical experiments in the INS/GPS integrated navigation.https://ieeexplore.ieee.org/document/8935212/Fitting transformationfitting H-infinity Kalman filterstability analysisuncertain system |
| spellingShingle | Juan Xia Shesheng Gao Yongmin Zhong Jiahao Zhang Chengfan Gu Yang Liu A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation IEEE Access Fitting transformation fitting H-infinity Kalman filter stability analysis uncertain system |
| title | A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation |
| title_full | A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation |
| title_fullStr | A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation |
| title_full_unstemmed | A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation |
| title_short | A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation |
| title_sort | novel fitting h infinity kalman filter for nonlinear uncertain discrete time systems based on fitting transformation |
| topic | Fitting transformation fitting H-infinity Kalman filter stability analysis uncertain system |
| url | https://ieeexplore.ieee.org/document/8935212/ |
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