湖北汽车工业学院学报2026，Vol.40Issue(2)：7-13,7.DOI:10.3969/j.issn.1008-5483.2026.02.002
基于改进自适应容积卡尔曼滤波的车辆状态估计
Vehicle State Estimation Based on Improved Adaptive Cubature Kalman Filter
摘要
Abstract
An improved adaptive cubature Kalman filter(IACKF)algorithm was proposed to address the issue of reduced parameter identification and state estimation accuracy in strong nonlinear systems with non-Gaussian tail noise,which is a challenge for the traditional cubature Kalman filter(CKF).Based on a three-degree-of-freedom vehicle dynamics model,the distribution and weights of cubature points were designed,and the square root form of the covariance matrix was employed to enhance nu-merical stability.Additionally,recursive least squares were used for online joint estimation of tire side slip stiffness,achieving adaptive adjustment of algorithm parameters.Comparative experiments were conducted through MATLAB/CarSim co-simulation under double lane-change and ramp conditions.The results show that IACKF significantly outperforms traditional CKF in estimating a vehicle's lateral slip angle,yaw rate,and longitudinal speed under strong nonlinear conditions,demonstrating its robust-ness in vehicle parameter estimation.关键词
状态估计/递推最小二乘法/容积卡尔曼滤波Key words
state estimation/recursive least squares/cubature Kalman filter分类
交通工程引用本文复制引用
李应明,周红妮,赵培亮..基于改进自适应容积卡尔曼滤波的车辆状态估计[J].湖北汽车工业学院学报,2026,40(2):7-13,7.基金项目
湖北省教育厅科学技术研究计划项目(Q201918050) （Q201918050）
湖北省创新基金(2015XTZX0414) （2015XTZX0414）
