Joint estimation of states and parameters of vehicle model using cubature Kalman filter

Abstract

Road condition is changed in the vehicle motion, unknown parameter is required in real-time estimation. Considering the unknown parameters of the vehicle dynamic system, For the purpose of pursuing accuracy estimation of vehicle states, this paper propose two kinds of strategies to estimate the state and parameter simultaneously, the joint cubature kalman filter and the dual cubature kalman filter. Firstly of all, a nonlinear double vehicle dynamic model based on Magic tire model is introduced. And then the algorithm of the joint cubature kalman filter (JCKF) and the dual cubature kalman filter (DCKF) are applied to vehicle state estimation. Finally, simulations are carried out using the Carsim and Matlab/Simulink to evaluate the algorithms. Simulation results demonstrate that the DCKF algorithm has relatively high accuracy and performances better than the JCKF under various road conditions.