Estimation of vehicle state based on improved dual layer UKF

In order to address the issue of lower estimation accuracy of traditional methods, an adaptive dual layer unscented Kalman filter algorithm (ADLUKF) is proposed, which combines the dual layer unscented Kalman filter (DLUKF) with an improved Sage-Husa algorithm to estimate the states and reduce the error in vehicle driving state estimation. The Carsim and Matlab/Simulink for joint simulation is applied and real vehicle test is established to verify the effectiveness of the estimator, and compare it with the Unscented Kalman Filter (UKF) algorithm. The results indicate that the ADLUKF algorithm can improve the estimation accuracy of vehicle estimation effectively.