Progressive Gaussian filtering for nonlinear uncertain systems based on Gaussian process models

Abstract

This paper studies the problem of progressive Gaussian filtering (PGF) for nonlinear uncertain systems, and a PGF method is proposed by incorporating Gaussian process (GP) models to improve the compensation ability for measurement uncertainties. Firstly, the measurements are classified by the Chi-square test, and an adaptive strategy for controlling the pseudo-duration of progressive measurement update is designed to compensate for measurement uncertainties. Moreover, a conservative upper bound of the pseudo-duration is given to obtain conservative estimates. Secondly, to mitigate the adverse effects caused by measurement uncertainties on state predictions, the GP model is incorporated into the PGF. Finally, the effectiveness and superiority of the proposed method are validated through simulation results.