KALMAN FILTERS FOR NON-UNIFORMLY SAMPLED MULTIRATE SYSTEMS

Abstract This paper proposes Kalman filter algorithms, including one-step prediction and filtering, for non-uniformly sampled multirate systems. The stability and convergence of the algorithms are analyzed, and their application to fault detection as well as state estimation in the framework of irregularly sampled data is investigated. Numerical examples are provided to demonstrate the applicability of the newly proposed algorithms.

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