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
祁登亮 1冯静安 1倪向东 1宋宝2
作者信息
- 1. 石河子大学 机械电气工程学院,新疆石河子 832003
- 2. 华中科技大学 机械科学与工程学院,武汉 430074
- 折叠
摘要
针对传统卡尔曼滤波在非高斯环境下对车辆状态估计鲁棒性和精度差的问题,提出最大相关熵准则(MCC)下改进自适应迭代扩展卡尔曼(AIEKF)滤波算法(MC-AIEKF),建立横-纵耦合的三自由度车辆模型,利用易测得的车载传感器信息设计了包含横摆角速度、质心侧偏角、纵向车速的状态观测器.在双移线和正弦扫频输入工况下通过Simulink/CarSim仿真试验平台对提出的算法进行了验证.结果表明,在非高斯环境下,相比于扩展卡尔曼滤波(EKF)和AIEKF,MC-AIEKF算法估计精度高,鲁棒性好,在实际的车辆状态估计中MC-AIEKF具有更强的适用性.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据