On accurate estimation of vehicle lateral states based on an improved adaptive unscented Kalman filter

Abstract

This paper proposes an improved adaptive unscented Kalman filter (iAUKF)-based vehicle lateral state estimation method. A three-degree-of-freedom vehicle dynamics model is first established. Second, the influence of process noise and measurement noise on vehicle lateral state estimation using standard UKF is analyzed, and a new type of normalized innovation square-based adaptive noise covariance adjustment strategy is designed and incorporated into the standard UKF to form the iAUKF algorithm with the purpose of achieving accurate estimation of vehicle lateral states. Finally, a comparative simulation investigation using CarSim and MATLAB/Simulink is conducted to validate the effectiveness of the proposed method, and the results show that the proposed iAUKF-based estimation method has higher accuracy and stronger robustness against the standard UKF algorithm.