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.
Research on vehicle state estimation based on dual unscented Kalman filter
International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023) ; 2023 ; Changsha, China
Proc. SPIE ; 12707
08.06.2023
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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