An Enhanced Adaptive Unscented Kalman Filter for Vehicle State Estimation
Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle es...
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| Vydáno v: | IEEE transactions on instrumentation and measurement Ročník 71; s. 1 - 12 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9456, 1557-9662 |
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| Abstract | Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle estimation under unknown noise conditions. The general exponential attenuation adaptive Kalman filter algorithm does not attenuate the historical data enough when the noise statistics change rapidly, thus leading to the state variable's inaccurate estimation. To improve the estimation accuracy of vehicle state variables, the exponential attenuation factor <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula> was further designed according to the variation of noise variance, and the influence of the latest data on state estimation was more considered. Based on the longitudinal dynamics modeling, the EAUKF method is applied to vehicle state estimation. Compared with the standard exponential weighted adaptive Kalman filtering algorithm and the average weighted adaptive Kalman filtering algorithm, the state variable estimation accuracy of the vehicle in this article is improved. |
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| AbstractList | Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle estimation under unknown noise conditions. The general exponential attenuation adaptive Kalman filter algorithm does not attenuate the historical data enough when the noise statistics change rapidly, thus leading to the state variable’s inaccurate estimation. To improve the estimation accuracy of vehicle state variables, the exponential attenuation factor [Formula Omitted] was further designed according to the variation of noise variance, and the influence of the latest data on state estimation was more considered. Based on the longitudinal dynamics modeling, the EAUKF method is applied to vehicle state estimation. Compared with the standard exponential weighted adaptive Kalman filtering algorithm and the average weighted adaptive Kalman filtering algorithm, the state variable estimation accuracy of the vehicle in this article is improved. Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle estimation under unknown noise conditions. The general exponential attenuation adaptive Kalman filter algorithm does not attenuate the historical data enough when the noise statistics change rapidly, thus leading to the state variable's inaccurate estimation. To improve the estimation accuracy of vehicle state variables, the exponential attenuation factor <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula> was further designed according to the variation of noise variance, and the influence of the latest data on state estimation was more considered. Based on the longitudinal dynamics modeling, the EAUKF method is applied to vehicle state estimation. Compared with the standard exponential weighted adaptive Kalman filtering algorithm and the average weighted adaptive Kalman filtering algorithm, the state variable estimation accuracy of the vehicle in this article is improved. |
| Author | Lu, Biliang Zhang, Ying Li, Ming Sun, Qingshuai Zhang, Yingjie Hu, Zuolei |
| Author_xml | – sequence: 1 givenname: Yingjie orcidid: 0000-0002-4170-6152 surname: Zhang fullname: Zhang, Yingjie email: zhangyj@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 2 givenname: Ming orcidid: 0000-0002-2505-2363 surname: Li fullname: Li, Ming email: minglee@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 3 givenname: Ying orcidid: 0000-0002-7557-2965 surname: Zhang fullname: Zhang, Ying email: ying_zhang@nwpu.edu.cn organization: School of Computer Science, Northwestern Polytechnical University, Xi'an, China – sequence: 4 givenname: Zuolei orcidid: 0000-0002-0635-7436 surname: Hu fullname: Hu, Zuolei email: huzuolei@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 5 givenname: Qingshuai orcidid: 0000-0001-6145-0599 surname: Sun fullname: Sun, Qingshuai email: sunqs@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 6 givenname: Biliang orcidid: 0000-0002-6023-295X surname: Lu fullname: Lu, Biliang email: lubiliang@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China |
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| SubjectTerms | Accuracy Adaptive algorithms Adaptive modulation factor Algorithms Attenuation Dynamic control enhanced adaptive unscented Kalman filter (EAUKF) Force Kalman filters longitudinal dynamics modeling Mathematical models Roads safe driving and dynamic control State estimation State variable Tires Vehicle dynamics vehicle state estimation Wheels |
| Title | An Enhanced Adaptive Unscented Kalman Filter for Vehicle State Estimation |
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