基于改进自适应容积卡尔曼滤波的车辆状态估计OA

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.