论文信息 - Kalman—Type Filtering for Stochastic Systems with State—Dependent Noise and Markovian Jumps

Kalman—Type Filtering for Stochastic Systems with State—Dependent Noise and Markovian Jumps

Abstract The paper presents a Kalman–type filtering problem for linear stochastic systems subjected both to state–dependent white noise and to Markovian jumps. The results are derived using a unified approach for the continuous–time case and for the discrete-time models of the plant. It is proved that the optimal filters gains depend on the solutions of some specific Riccati-type systems which generalize the well–known equations from the classical Kalman filtering.

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