Vehicle state estimation based on extended Kalman filter and radial basis function neural networks
To improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221102730 |
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| _version_ | 1832553257944219648 |
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| author | Yunfei Zha Xinye Liu Fangwu Ma CC Liu |
| author_facet | Yunfei Zha Xinye Liu Fangwu Ma CC Liu |
| author_sort | Yunfei Zha |
| collection | DOAJ |
| description | To improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. The weights of the estimation results of different algorithms are assigned through the dichotomy. The redundancy and fusion of estimation algorithms can improve estimation performance. The effectiveness of the fusion method is verified by the co-simulation of MATLAB/Simulink and CarSim, and the real vehicle test. The results show that the change trend of the estimation result is consistent with the actual state parameters change trend, and the estimation accuracy after algorithm fusion is significantly improved compared to a single extended Kalman filter or radial basis function. |
| format | Article |
| id | doaj-art-56420b1d393f4f43b97d84a5711c38bb |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-56420b1d393f4f43b97d84a5711c38bb2025-02-03T05:54:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-06-011810.1177/15501329221102730Vehicle state estimation based on extended Kalman filter and radial basis function neural networksYunfei Zha0Xinye Liu1Fangwu Ma2CC Liu3Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, ChinaTo improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. The weights of the estimation results of different algorithms are assigned through the dichotomy. The redundancy and fusion of estimation algorithms can improve estimation performance. The effectiveness of the fusion method is verified by the co-simulation of MATLAB/Simulink and CarSim, and the real vehicle test. The results show that the change trend of the estimation result is consistent with the actual state parameters change trend, and the estimation accuracy after algorithm fusion is significantly improved compared to a single extended Kalman filter or radial basis function.https://doi.org/10.1177/15501329221102730 |
| spellingShingle | Yunfei Zha Xinye Liu Fangwu Ma CC Liu Vehicle state estimation based on extended Kalman filter and radial basis function neural networks International Journal of Distributed Sensor Networks |
| title | Vehicle state estimation based on extended Kalman filter and radial basis function neural networks |
| title_full | Vehicle state estimation based on extended Kalman filter and radial basis function neural networks |
| title_fullStr | Vehicle state estimation based on extended Kalman filter and radial basis function neural networks |
| title_full_unstemmed | Vehicle state estimation based on extended Kalman filter and radial basis function neural networks |
| title_short | Vehicle state estimation based on extended Kalman filter and radial basis function neural networks |
| title_sort | vehicle state estimation based on extended kalman filter and radial basis function neural networks |
| url | https://doi.org/10.1177/15501329221102730 |
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