中文摘要：

车辆行驶中某些状态参量不易准确测得或测量成本较高,而这些变量的准确获取对车辆底盘控制有着重要的意义。为以较低成本获取重要的车辆运动状态,建立包括横摆、侧向和纵向3自由度的非线性车辆模型,利用扩展Kalman滤波（Extended Kalman filtering,EKF）理论建立了信息融合算法,给出车辆状态变量最小方差意义下的融合结果,利用少量的易测车辆状态信息（转向盘转角、车辆纵、侧向加速度）融合得出所需的难测车辆状态（横摆角速度、质心侧偏角）。并在Matlab/Simulink环境下利用实车场地试验数据进行了离线仿真。多种工况下的场地试验结果表明,该算法在估计汽车横摆角速度、质心侧偏角、纵向速度时具有一定的准确性,特别是对横摆角速度的估计,即使在车辆非线性区也表现出良好性能。同时该融合算法简单、稳定及所需融合输入较少的特点使该算法在实际中的应用成为可能。

英文摘要：

Some state variables of a vehicle in running are not easy to measure accurately or cheaply, however these variables are of great significance to chassis control. A nonlinear 3 degree-of-freedom vehicle model including yaw motion, longitudinal motion and side motion is set up, and an information fusion algorithm based on extended Kalman filtering （EKF） theory is established, which gives out a fusion result of vehicle state variables at minimum square error. Fusing a few state variables of vehicles （steering wheel angle, longitudinal acceleration and lateral acceleration）, the needed variables of the vehicle （yaw rate and sideslip angle） are procured. Off line simulation is carried out in Matlab/simulink environment by using real vehicle field test data. The algorithm is accurate in estimating longitudinal rate, side slip angle, and especially yaw rate, and shows good performance even in the nonlinear zone of the vehicle. The algorithm is simple and stable, and needs less fusion input, so it is possible to apply it to actual vehicle control.