由于部分汽车状态参数无法直接通过传感器获得，为了提高这些参数的估计精度以准确判断汽车行驶过程中的状态变化，增强控制系统的鲁棒性，文中提出了基于无迹卡尔曼滤波的汽车状态参数估计方法． 该方法在传统卡尔曼滤波算法的基础上，采用无迹卡尔曼滤波算法对汽车质心侧偏角、横摆角速度、路面附着系数等状态参数进行估计，并运用 Simulink 与 Carsim 进行联合仿真． 结果表明，无迹卡尔曼滤波算法响应快，估计精度较扩展卡尔曼滤波高，能满足车辆高级动力学控制系统的控制需要．

In order to improve the estimation accuracy of some vehicle state parameters that can not be obtained by sensors directly and thus to estimate the state variation of running vehicles accurately,a method on the basis of un- scented Kalman filtering (UKF) is proposed,which helps enhance the robustness of vehicle control system.In this method,an UKF algorithm on the basis of traditional Kalman filtering is developed to estimate such vehicle state parameters as side slip angle,yaw rate and road adhesion coefficient,and a simulation by using both Simulink and Carsim software is carried out.The results indicate that the proposed UKF is superior to the extended Kalman filte- ring for its short response time and high estimation accuracy.Thus，it can meet the requirements of advanced dy- namic control system of vehicles.