基于改进双重无迹卡尔曼滤波算法的车辆状态估计  

Vehicle state estimation based on improved dual unscented Kalman filter

作　　者：费明哲 王健[1] 于金鹏[2] 杨君[1] 杜若飞 王云靖 邓欢 FEI Mingzhe;WANG Jian;YU Jinpeng;YANG Jun;DU Ruofei;WANG Yunjing;DENG Huan(School of Automotive Engineering,Shandong Jiaotong University,Jinan 250357,China;School of Automation,Qingdao University,Qingdao 266071,China)

机构地区：[1]山东交通学院汽车工程学院,山东济南250357 [2]青岛大学自动化学院,山东青岛266071

出　　处：《山东交通学院学报》2023年第3期7-14,共8页Journal of Shandong Jiaotong University

基　　金：山东省交通运输厅科技计划项目(2022B107);山东省高等学校青创科技支持计划项目(2021KJ039);山东交通学院研究生科技创新项目(2022YK001)。

摘　　要：针对车辆行驶过程中的状态和参数估计问题,基于软件MATLAB中车辆三自由度动力学模型,分别采用无迹卡尔曼滤波(unscented Kalman filter,UKF)算法、双重无迹卡尔曼滤波(dual unscented Kalman filter,DUKF)算法及采用奇异值分解的改进双重无迹卡尔曼滤波(singular value decomposition-dual unscented Kalman filter,SVD-DUKF)算法,估计车辆在同一工况下的状态及参数。结果表明:UKF算法能保证一定的估计精度,但需时刻输入准确的车身质量等参数,在车辆行驶过程中难以实现;DUKF算法与SVD-DUKF算法有相近的估计精度,但DUKF算法采用Cholesky分解,在车辆运行过程中难以保证误差协方差矩阵为正定矩阵;SVD-DUKF算法更适合估计车辆行驶状态及参数,估计精度较高,适用性较强。Based on the 3-DOF dynamics model of the vehicle in MATLAB software,the unscented Kalman filter(UKF),the dual unscented Kalman filter(DUKF)and the improved dual unscented Kalman filter(SVD-DUKF)with singular value decomposition are used to estimate the state and parameters of the vehicle under the same operating conditions respectively.The results show that the UKF algorithm can achieve high estimation accuracy,but the premise is to input accurate parameters such as body mass at all times,which is difficult to reach during the operation of vehicle.The estimation accuracy of DUKF algorithm is similar to SVD-DUKF algorithm,but the former is difficult to guarantee that the error covariance matrix is a positive definite matrix during the operation of vehicle because it uses Cholesky decomposition.The latter with higher estimation accuracy and applicability is more suitable for estimating the driving state and parameters of a vehicle.

关 键 词：状态估计 UKF算法 DUKF算法 奇异值分解 

分 类 号：U463.9[机械工程—车辆工程]