基于容积卡尔曼滤波的车辆行驶状态估计

Study on Vehicle Driving State Estimation Based on Cubature Kalman Filter

目前针对车辆行驶状态的估计主要采用的是扩展卡尔曼滤波、无轨迹卡尔曼滤波、粒子滤波算法及其它们的改进方法。针对车辆行驶过程中的状态估计问题,论文提出了基于容积卡尔曼滤波的车辆行驶状态估计算法。建立了非线性三自由度车辆估算模型和Dugoff轮胎模型,通过对纵向加速度、侧向加速度、横摆角速度、方向盘转角和轮速传感器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,并应用Car Sim和Matlab/Simulink联合仿真实验对算法进行仿真验证。结果表明:基于容积卡尔曼滤波的估计算法能够较准确地、稳定地对车辆行驶状态进行估计。

At present in view of that the vehicle state estimation mainly uses extended kalman filtering,no track kalman filter and particle filter algorithm and its improved method.For the vehicle driving state estimation problem in the process of vehicle driving,the algorithm is proposed to estimate the vehicle driving state based on Cubature Kalman Filter.The nonlinear 3-DOF model and Dugoff tire model are established,The vehicle driving state is estimated accurately through information fusion of the longitudinal acceleration,lateral acceleration,yaw rate and steering wheel angle sensor signals.The algorithm is verified by CarSim and Matlab/Simulink co-simulation.The results show that the estimation algorithm can accurately and stably estimate the vehicle driving state.