Research on vehicle state estimation based on dual unscented Kalman filter

Mingzhe Fei; Jian Wang; Jun Yang; Ruofei Du; Yunjing Wang; Huan Deng

doi:10.1117/12.2680955

8 June 2023 Research on vehicle state estimation based on dual unscented Kalman filter

Mingzhe Fei, Jian Wang, Jun Yang, Ruofei Du, Yunjing Wang, Huan Deng

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270725 (2023) https://doi.org/10.1117/12.2680955
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Due to the strong nonlinear characteristics of vehicle system, obtaining the current vehicle motion state accurately is the basis for improving vehicle control accuracy. Moreover, with the increase or decrease of passengers and goods, some parameters of the vehicle will change, and accurate values cannot be obtained at any time. In this paper, the Dual Unscenter Kalman filtering algorithm (DUKF) is compared with the Unscented Kalman filtering algorithm (UKF) by using the vehicle dynamics grey box model in Matlab. The results show that DUKF still has a certain estimation accuracy when estimating vehicle state and parameters.

Citation Download Citation

Mingzhe Fei, Jian Wang, Jun Yang, Ruofei Du, Yunjing Wang, and Huan Deng "Research on vehicle state estimation based on dual unscented Kalman filter", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270725 (8 June 2023); https://doi.org/10.1117/12.2680955

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