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 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.

Automated summary

An adaptive fading unscented Kalman filter (AFUKF) algorithm was proposed for vehicle state estimation. A 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established, and a vehicle state estimator based on Kalman filter was designed. Simulation verification showed the effectiveness and reliability of the designed estimator for vehicle state estimation.