Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter

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 fil...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2022; pp. 1 - 11
Main Authors Liu, Yingjie, Cui, Dawei
Format Journal Article
LanguageEnglish
Published New York Hindawi 26.04.2022
Hindawi Limited
Subjects
Accuracy
Algorithms
Electric vehicles
Fading
Kalman filters
Mathematical models
Noise
State estimation
Tires
Traffic accidents & safety
Velocity
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/7355110