Novel Modeling and Filtering for Stochastic Switched Systems With Heterogeneous Behaviors

Abstract

A novel heterogeneous behavior representation for linear stochastic switched system is proposed in the discrete-time domain. The switching modes of state evolution and measurement output are described by two random sequences with known probability information. The more general and flexible framework covers several classes of well-studied models as special cases, and can be served to manage different complex systems with random abrupt changes in structure and parameter, so that it has wider applicability than existing models. By introducing an equivalent auxiliary system in virtue of the mode-dependent random parameter matrices, the filter design schemes, including an optimal and a suboptimal recursive algorithms, are performed for the established model in the minimum mean square error sense to meet different application requirements. Illustrative numerical examples demonstrate the effectiveness of the proposed formulation and the corresponding filters that enjoy a promising application prospect.INDEX TERMS State estimation, minimum mean square error, heterogenous, switching modes, random parameters matrices.