Hao Yan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hao Yan has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Yong Chen
Mechanical Electrical Engineering School, Beijing Information Science and Technology University - Qinghe Xiaoying Campus, Beijing, China
1 shared publication - Yuecheng Li
School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 25 June 2023
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 14 (7) , 167
Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering
To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.
- 25 June 2023
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 14 (7) , 167
Open AccessVehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering
To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.