Quadratic filtering for non-Gaussian stochastic parameter systems with buffer-aided strategy

Abstract

This paper is concerned with the quadratic filtering problem for a class of linear discrete time-varying systems with non-Gaussian stochastic parameters and intermittent measurements. In order to improve the filtering performance, a buffer-aided strategy is used to store the historical measurement data which is sent to the filter via a shared communication channel at each transmission instant. The main objective of this paper is to design a quadratic filter to estimate the states of the considered non-Gaussian stochastic systems by using the buffer-aided strategy. Specifically, an augmented system is constructed by combining the original system states and their second-order Kronecker powers. As such, the considered quadratic filtering problem amounts to the Kalman filtering problem for the augmented system. The filtering error covariance (FEC) conditioned on the transmission intervals is firstly derived recursively and then is minimized by choosing the suitable filtering gains. Moreover, sufficient conditions are deduced to guarantee the boundedness of both the FEC and the conditional FEC. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed filtering method.