Vehicle State Estimation Algorithm for Intelligent Chassis Control with Noise Statistics Updates

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.