基于IEKF-APF算法的汽车状态估计

行驶汽车状态变量质心侧偏角和横摆角速度是汽车稳定性控制系统中重要控制变量，准确获取行驶过程中的状态信息是汽车控制系统研究的关键问题。应用估计理论由传感器测出易测变量来估计难以测量的关键状态变量是一种常用的估计方法。提出一种新的粒子滤波算法通过所建立的包含定常平稳随机噪声和非线性轮胎的汽车动力学7自由度整车模型对汽车状态进行估计。针对粒子滤波过程中出现的退化问题，应用迭代扩展卡尔曼滤波融入最新观测信息产生更加接近真实状态的重要性密度函数，辅助粒子滤波算法通过所产生的重要性密度函数结合观测量进行重采样，结合这两种算法提出迭代扩展卡尔曼-辅助粒子滤波算法(Iterative extended Kalman filtering-auxiliary particle filtering algorithm, IEKF-APF)以改善粒子采样和估计精度的提高。为验证所提出的IEKF-APF算法估计性能，将其结果与实车试验结果和无迹卡尔曼滤波算法(Unscented Kalman filtering, UKF)估计结果进行比较，结果表明其估计性能优于UKF，更接近于试验结果。

Vehicle State Estimation Based on IEKF-APF

Side slip angle and yaw rate are the important control parameters of vehicle stability control system, and getting accurate state information of driving process is the key issue of control system research. A common estimation method based on the estimation theory is that using sensors to get easily measured variables, and then estimating the key state variables which are difficult to measure. A new particle filtering algorithm is proposed to estimate vehicle key states with a 7-DOF nonlinear vehicle dynamic model which contained constant noise and nonlinear tire model. For particle degradation during particle filtering process, the iterative extended Kalman filtering algorithm is used to produce importance density function which is more close to the true state, and auxiliary particle filtering algorithm with the latest observation information is used to resample particle with the observation. The iterative extended Kalman filtering-auxiliary particle filtering algorithm(IEKF-APF) combines of the above two algorithms to improve the particle resampling and estimation precision. To validate the estimation performance of IEKF-APF, compare the estimation results of IEKF-APF simultaneously with road test values and unscented Kalman filtering algorithm(UKF) estimation results, and the comparison shows that IEKF-APF estimation performance is better than that of UKF, and its estimation results are closer to the test results.