TY  - JOUR
PY  - 2023//
TI  - Research on vehicle state estimation based on robust adaptive unscented particle filter
JO  - International journal of vehicle safety
A1  - Wang, Yalin
A1  - Cui, Dawei
A1  - Liu, Yingjie
SP  - 1
EP  - 18
VL  - 13
IS  - 1
N2  - In order to reduce the influence of historical measurement data errors, a filter estimation method of vehicle state named Robust Adaptive Unscented Particle Filter (RAUPF) is proposed. Firstly, a 3-DOF non-linear vehicle dynamics model was established. Then, a joint simulation platform was established. At the same time, the simulation was conducted under three different operating conditions: the sine delay test and the double lane change test and the slop input test. The results showed that compared to the Unscented Particle Filter (UPF) algorithm, the Root Mean Square Error (RMSE) and average absolute error (MAE) of the estimation value of the RAUPF are smaller. And also, compared to the UPF algorithm, the robustness of the RAUPF method is better. The proposed RAUPF algorithm can effectively suppress noise fluctuations and improve estimation accuracy.     Keywords: vehicle engineering; state estimation; RAUPF.<p />  <p>Language: en</p>
LA  - en
SN  - 1479-3105
UR  - http://dx.doi.org/10.1504/IJVS.2023.137655
ID  - ref1
ER  -