Sideslip angle, yaw rate, and vehicle speed are critical for intelligent chassis control. Existing vehicle state estimation studies seldom simultaneously consider the effects of data loss and noise variations on estimation accuracy. To address these issues, we propose the expectation-maximizing robust extended Kalman filter (EMREKF). First, we develop the robust extended Kalman filter (REKF) to handle the effects of missing measurements. Then, we propose an expectation-maximization-based noise dynamic update method to adjust the noise parameters of the REKF. Simulation results show that the EMREKF outperforms both the conventional REKF and EKF.
Vehicle State Estimation Algorithm for Intelligent Chassis Control with Noise Statistics Updates
2024-10-18
2633953 byte
Conference paper
Electronic Resource
English
Intelligent control method and device for vehicle chassis, vehicle chassis and vehicle
European Patent Office | 2024
|Intelligent chassis state parameter estimation system based on vehicle dynamics model
European Patent Office | 2024
|Chassis vehicle, chassis vehicle control system and chassis vehicle control method
European Patent Office | 2021
|Monitoring method and device for intelligent chassis, intelligent chassis and vehicle
European Patent Office | 2024
|Vehicle chassis control method and device and intelligent vehicle
European Patent Office | 2024
|