A novel state estimation approach for suspension system with time-varying and unknown noise covariance

Abstract In this paper, a novel state estimation approach based on variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for suspension system with the time-varying and unknown noise covariance. Using the VB approach, the time-varying noise covariance can be inferred from the inverse-Wishart distribution, then optimized state estimation by finite sampling posterior probability distribution function (PDF) of noise covariance and backward Kalman smoothing. In addition, a new road classification algorithm based on multi-objective optimization and linear classifier is proposed to identify the unknown noise covariance. Simulation results for a suspension model with time-varying and unknown noise covariance show that the proposed approach has a higher performance in state estimation accuracy than other filters.