基于信息融合技术的车辆行驶状态估计  

Study of Vehicle Driving State Estimation Based on Information Fusion

作　　者：李刚[1] 解瑞春 卫绍元[1] 韩海兰[1] 

机构地区：[1]辽宁工业大学汽车与交通工程学院,辽宁锦州121001

出　　处：《计算机仿真》2014年第10期183-186,共4页Computer Simulation

基　　金：国家自然科学基金青年科学基金项目(E51305190);辽宁省教育厅项目(L2013253)

摘　　要：针对车辆行驶状态估计问题,基于扩展卡尔曼滤波(EKF)理论设计了汽车横摆角速度、质心侧偏角和纵向车速的估计算法。建立了车辆非线性三自由度估算模型,应用扩展卡尔曼滤波对纵向加速度、侧向加速度和方向盘转角低成本传感器信号的信息融合,实现对车辆行驶状态的准确估计。选择高速双移线工况,通过Car Sim与Matlab/Simulink联合仿真对算法进行仿真验证。结果表明:算法能够准确估计车辆的横摆角速度、质心侧偏角和纵向车速,并具有良好的实时性。An estimation algorithm is presented based on extended Kalman filter （EKF） for vehicle states such as vehicle longitudinal speed, side slip angle and yaw rate. A non - linear estimation model is established for three de- grees of freedom vehicle, the extended Kalman filter is applied to information fusion of low - cost sensor signals inclu- ding the longitudinal acceleration, lateral acceleration and steering wheel angle, and the accurate estimates of the ve- hicle states are achieved. Simulation experiments are given under high - speed double lane conditions by using Car- Sim and Matlab / Simulink co - simulation algorithms. The results show that the algorithm can accurately estimate the longitudinal vehicle speed, yaw rate and slip angle.

关 键 词：扩展卡尔曼滤波 车辆状态估计 信息融合 联合仿真 

分 类 号：U463.4[机械工程—车辆工程]