Vehicle state estimation based on adaptive double-layer untracked Kalman filter
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.
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