What question did this study set out to answer?

To enhance the accuracy of vehicle state estimation using an improved dual layer unscented Kalman filter algorithm.

February 10, 2026Open Access

Estimation of vehicle state based on improved dual layer UKF

QWQianqian Wang YLYingjie Liu DCDawei Cui

Key Points

To enhance the accuracy of vehicle state estimation using an improved dual layer unscented Kalman filter algorithm.
Proposed a combined dual layer unscented Kalman filter with an improved Sage-Husa algorithm.
Utilized Carsim and Matlab/Simulink for joint simulation.
Conducted real vehicle tests to validate the estimator's effectiveness.
Compared performance with the traditional unscented Kalman filter.
ADLUKF algorithm significantly improves estimation accuracy compared to traditional methods.
Experiments demonstrated reduced error in vehicle driving state estimation.

Abstract

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.

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