基于迭代扩展卡尔曼滤波的车辆运动状态估计

Vehicle Motion State Estimation Based on Iterative Extended Kalman Filter

采用低成本传感器并借助卡尔曼滤波方法实现车辆运动状态的高精度估计。首先考虑车辆侧向运动、横摆运动以及侧倾运动,建立非线性三自由度的动力学车辆模型,通过对其线性化,实现扩展卡尔曼滤波设计,进一步针对线性化带来的截断误差问题,利用贝叶斯估计建立极大后验状态估计最小二乘表达式,通过进一步求解最终设计完成了迭代扩展卡尔曼滤波算法。通过不同行驶条件下仿真,验证迭代扩展卡尔曼滤波过滤噪声和追踪实际值的能力。仿真结果表明:在复杂的行驶条件下,迭代扩展卡尔曼滤波能大幅过滤噪声,并有效追踪车辆质心侧偏角和横摆角速度的实际状态。

The paper discusses to use low-cost sensors and Kalman filter to achieve high-precision vehicle motion state estimation.Firstly,considering the lateral,yaw motion and roll motion of the vehicle,a nonlinear 3-DOF dynamic vehicle model is established and further linearized to realize extended Kalman filter design.To solve the problem of truncation error caused by linearization,the least square expression of largest posterior state estimation is established by Bayesian estimation,and the iterative extended Kalman filter(IEKF)algorithm is finally completed by further solving.Through simulations under different driving conditions,the IEKF algorithm is verified on filtering noise and tracking the actual value.The simulation results show that under complex driving conditions the IEKF algorithm can effectively filter noise and track the actual state of the vehicle slip angle and yaw rate.