Recursive Robust Filtering with Finite-Step Correlated Process Noises and Missing Measurements

In this paper, the robust filter design problem is studied for a class of uncertain dynamical systems with finite-step correlated process noises and missing measurements. The dynamical system under consideration is subject to both deterministic norm-bounded uncertainties in the measurement output and stochastic uncertainties on the system states. The process noises are assumed to be finite-step correlated. The missing measurement phenomenon is modeled as a binary switching sequence. Based on the min-max game theory, a recursive robust filter is designed that is suitable for online application. A particular feature is that, as the proposed robust filters work in a recursive fashion, there is no need to investigate the existence issue of the filters. A simulation example is presented to illustrate the usefulness of the proposed filter.

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