Minimum-Variance Recursive Filtering for Two-Dimensional Systems With Degraded Measurements: Boundedness and Monotonicity

This paper addresses the recursive minimum-variance filtering problem for a class of two-dimensional shift-varying systems with stochastic nonlinearity and degraded measurements. The stochastic nonlinearity is governed by its statistical characteristics and the degraded measurements reflect the signal degradation obeying certain prescribed probabilistic distributions. The main objective of this paper is to construct a two-step recursive filter that achieves the minimum error variance of the state estimation at each step. Utilizing an inductive approach, unbiasedness of the proposed filter is first ensured and the parameters of the filter are then designed by resorting to the completing squares method. Subsequently, the filtering performances including the boundedness and the monotonicity are investigated with respect to the measurement degradations through mathematically rigorous analysis. Moreover, a computational algorithm is presented to facilitate the online implementation of the designed filter. Finally, numerical simulation illustrates the effectiveness and applicability of the proposed filtering scheme in the state estimation problem for monitoring a long transmission line in circuit systems.