机械科学与技术2024，Vol.43Issue(4)：573-581,9.DOI:10.13433/j.cnki.1003-8728.20220288
最大相关熵准则下改进扩展卡尔曼滤波的车辆状态估计
Vehicle State Estimation with Improved Extended Kalman Filter Under Maximum Correntropy Criterion
摘要
Abstract
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.关键词
自适应迭代扩展卡尔曼滤波/车辆状态估计/最大相关熵准则/非高斯环境Key words
adaptive iterative extended Kalman filtering/vehicle state estimation/maximum correntropy criterion/non-Gaussian environment分类
交通工程引用本文复制引用
祁登亮,冯静安,倪向东,宋宝..最大相关熵准则下改进扩展卡尔曼滤波的车辆状态估计[J].机械科学与技术,2024,43(4):573-581,9.基金项目
国家自然科学基金项目(61663042) （61663042）
