State Estimation for Two-Dimensional Systems with Stochastic Nonlinearity and Measurement Delay

This paper addresses the state estimation problem for a class of two-dimensional discrete-time linear systems with stochastic nonlinearity and measurement delay. Utilizing the re-organized measurement approach, the original system is firstly converted to a delay-free system. Then a novel four-step recursive filter is proposed. The main aim of the addressed problem is construct a recursive filter to minimize the error covariance of the state estimation at each step. The optimal filter parameters are determined by utilizing the completing squares method.