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- Title
Vehicle state estimation based on limited memory random weighted extended Kalman filter.
- Authors
Hu Jingyu; Wang Yan; Yan Yongjun; Geng Keke; Bai Shuo; Yin Guodong
- Abstract
To reduce the influences of historical data errors on the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm was proposed. First, a three-degree-of-free-dom nonlinear vehicle dynamics model was established. Secondly, the limited memory filter and the extended Kalman filter(EKF) were fused to form the limited memory EKF. Then, according to the random weighting theory, the weighted coefficient which obeys Dirichlet distribution was designed to further improve the estimation accuracy of the filter. Finally, a CarSim and Matlab/Simulink co-simulation platform was established. Simulation tests were carried out under the high adhesion coefficient and the low adhesion coefficient. The results show that compared with the estimation results of the standard EKF algorithm, under the high adhesion simulation condition, the root mean square error of yaw rate, sideslip angle and longitudinal velocity estimated based on LMRWEKF algorithm is reduced by 60. 23%, 19. 63% and 91. 57%, respectively. Under the low adhesion simulation condition, the root mean square error of yaw rate, sideslip angle and longitudinal velocity estimated based on LMRWEKF algorithm is reduced by 59. 38%>, 13. 92%> and 94. 20%>, respectively. The LMRWEKF algorithm can effectively suppress the noise fluctuation and improve the estimation accuracy.
- Publication
Journal of Southeast University / Dongnan Daxue Xuebao, 2022, Vol 52, Issue 2, p387
- ISSN
1001-0505
- Publication type
Article
- DOI
10.3969/j.issn.1001-0505.2022.02.022