具有噪声鲁棒性的车辆质心侧偏角估计

Estimation of Vehicle's Sideslip Angle with Noise Robustness

为解决车辆运动状态估计中卡尔曼滤波器因量测噪声统计特性发生变化而出现性能降低甚至滤波发散的问题,针对双轨车辆模型提出了一种基于变分贝叶斯自适应容积卡尔曼滤波算法的估计方法。该方法从概率角度将车辆运动状态及量测噪声一起作为待估计的变量,通过定点迭代的方式不断逼近量测噪声的真实后验分布,最终获得局部最优解。基于CarSim与MATLAB/Simulink的联合仿真结果表明:在时变噪声协方差条件下,该算法与CKF相比具有精度高、性能稳定的优点。

In order to solve the performance degradation and even filter divergence problem of Kalman filter caused by the varying measurement noise statistical characteristics in vehicle motion state estimation,an estimation method based on variational Bayesian adaptive cubature Kalman filter is proposed for the dual-track vehicle model.This method takes the vehicle motion state and the measurement noise as the variables and to be estimated together,and continuously approximates the true posterior distribution of the measurement noise through fixed-point iteration,finally obtains the local optimal solution.The simulation results based on the CarSim-MATLAB/Simulink joint platform show that under the condition of Time-variant noise covariance,the algorithm has the advantages of high accuracy and stable performance compared with cubature Kalman filter.