Open AccessVehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter
Author(s) -
Yingjie Liu,
Dawei Cui
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/7355110
Subject(s) - kalman filter , estimator , fading , extended kalman filter , control theory (sociology) , state (computer science) , reliability (semiconductor) , unscented transform , computer science , nonlinear system , estimation , key (lock) , engineering , invariant extended kalman filter , algorithm , mathematics , artificial intelligence , statistics , control (management) , physics , systems engineering , quantum mechanics , decoding methods , power (physics) , computer security
Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area.
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