井冈山大学学报（自然科学版）2025，Vol.46Issue(2)：89-96,8.DOI:10.3969/j.issn.1674-8085.2025.02.011
自适应扩展卡尔曼滤波在车辆状态估计中的优化研究
OPTIMIZATION OF ADAPTIVE EXTENDED KALMAN FILTER IN VEHICLE STATE ESTIMATION
摘要
Abstract
In order to solve the problem that the system process noise and observation noise in the traditional extended Kalman filter(EKF)algorithm rely on artificial experience for parameter setting,an improved adaptive extended Kalman filter(AEKF)algorithm was proposed to estimate the vehicle state.Firstly,the nonlinear three-degree-of-freedom dynamics model and the H.B.Pacejke tire model were used as the estimation models.Then,the traditional system process noise and measurement noise covariance matrix were designed into an adaptive matrix that changed with the change of front wheel angle and vehicle speed to reduce the interference of external random noise.Finally,Carsim and Matlab/Simulink simulation software were used to simulate and verify the proposed algorithm under different working conditions,and compare it with the traditional EKF algorithm.The results show that compared with the traditional EKF algorithm,the improved AEKF algorithm can not only adapt to different working conditions and not be affected by external noise interference,but also improve the estimation accuracy.关键词
车辆状态估计/EKF算法/自适应控制/车辆动力学模型/H.B.Pacejke轮胎模型Key words
vehicle state estimation/EKF algorithm/adaptive control/vehicle dynamics model/H.B.Pacejke tire model分类
交通工程引用本文复制引用
张庭芳,凌勇,谢世坤,吴晓建,刘建胜..自适应扩展卡尔曼滤波在车辆状态估计中的优化研究[J].井冈山大学学报（自然科学版）,2025,46(2):89-96,8.基金项目
国家自然科学基金项目(52262054) （52262054）
