重庆理工大学学报2024，Vol.38Issue(13)：29-36,8.DOI:10.3969/j.issn.1674-8425(z).2024.07.004
自适应双层无迹卡尔曼滤波的车辆状态估计
Vehicle state estimation based on adaptive double-layer untracked Kalman filter
摘要
Abstract
Aiming at the issues such as estimation inaccuracy,poor robustness and system noise uncertainty of the unscented Kalman filter(UKF)algorithm in vehicle state estimation,an enhanced Sage-Husa adaptive double-layer unscented Kalman filter(ADLUKF)algorithm is proposed to estimate the yaw velocity and centroid side deflection angle of the vehicle.Through the enhanced Sage-Husa filter,the process noise and measurement noise of the system are dynamically adjusted to achieve the adaptive adjustment of the filter.Meanwhile,a double-layer unscented Kalman filter algorithm is employed to update the initial value of the outer UKF algorithm through the inner UKF algorithm,thereby enhancing the accuracy of the estimation system.To verify the effectiveness of the algorithm,a three-degree-of-freedom vehicle dynamics model is built.Based on this model,a vehicle state estimation algorithm based on ADLUKF and UKF is developed.The effectiveness of the algorithm is verified by using Carsim and Matlab/Simulink co-simulation and real vehicle test data.The results indicate that the ADLUKF algorithm has higher estimation accuracy and better stability compared with UKF.关键词
自适应双层无迹卡尔曼滤波/Sage-Husa/参数估计/横摆角速度/质心侧偏角Key words
adaptive double-layer unscented Kalman filter/Sage-Husa/parameter estimation/yaw velocity/side-slip angle分类
交通工程引用本文复制引用
徐劲力,张光俊..自适应双层无迹卡尔曼滤波的车辆状态估计[J].重庆理工大学学报,2024,38(13):29-36,8.基金项目
广西科技重大专项项目(桂科AA23062066) （桂科AA23062066）
