Vehicle State Estimation with Improved Extended Kalman Filter Under Maximum Correntropy Criterion
Because of the poor robustness and accuracy of the conventional Kalman filter for vehicle state estimation in the non-Gaussian environment,an improved adaptive iterative extended Kalman filtering(AIEKF)algorithm(MC-AIEKF)under the maximum correntropy criterion(MCC)is proposed.A three-degree-of-freedom lateral-longitudinal coupled vehicle model is established,and a state observer containing the yaw rate,mass-central sideslip angle and longitudinal speed of the vehicle is designed by utilizing the easily available information on onboard sensor.The proposed algorithm is verified with the Simulink/CarSim simulation platform under the conditions of double lane change and sine sweep input.The results show that the MC-AIEKF algorithm has higher estimation accuracy and better robustness than the extended Kalman filtering(EKF)and the AIEKF in the non-Gaussian environment,being more applicable for vehicle state estimation in real situations.
adaptive iterative extended Kalman filteringvehicle state estimationmaximum correntropy criterionnon-Gaussian environment
国家科技期刊平台
NSTL
万方数据
维普
