Vehicle State and Bias Estimation Based on Unscented Kalman Filter with Vehicle Hybrid Kinematics and Dynamics Models

作     者：Shouren Zhong Yang Zhao Linhe Ge Zitong Shan Fangwu Ma Shouren Zhong;Yang Zhao;Linhe Ge;Zitong Shan;Fangwu Ma

作者机构：State Key Laboratory of Automotive Simulation and ControlJilin UniversityChangchun 130022China 

出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)

年 卷 期：2023年第6卷第4期

页      面：571-585页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：funded by China Postdoctoral Science Foundation(Grant No.2020M670846) Foundation of State Key Laboratory of Automotive Simulation and Control(Grant No.20180104) Science and Technology Development Plan of Jilin Province(Grant No.YDZJ202102CXJD017) Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(Grant No.YESS20200139) 

主　　题：Vehicle state estimation Vehicle hybrid models Unscented Kalman filter Integration stability 

摘      要：In recent years,vehicle state estimation methods incorporating different vehicle models have received extensive *** the vehicle is disturbed by external forces not considered in traditional vehicle models(for example,a certain slope,or wind resistance different from theoretical calculation),the problem of model mismatch will occur,which leads to the inaccurate estimation of the vehicle *** solve this problem,an Unscented Kalman Filter(UKF)algorithm is used to fuse inertial navigation data with the vehicle hybrid model in this *** hybrid model introduces a switching strategy that fuses the vehicle kinematics and the dynamics models while augmenting biases that need to be estimated in the vehicle *** switching strategy resolves the integration divergence problem of vehicle dynamics models at low speeds and the inaccurate estimation of vehicle kinematics models at high *** experiments demonstrate that the proposed method can accurately estimate biases induced by external forces,enhancing the accuracy and confidence of states by eliminating errors caused by these *** robustness of the method is validated in vehicle verification platform experiments,where errors in vehicle lateral speed and yaw rate are reduced by 9.7 cm/s and 0.012°/s,respectively,under large curvature maneuvers,and 9.6 cm/s and 0.004°/s under quarter-turn *** proposed method significantly improves lateral speed and vehicle position accuracies.