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We consider discrete-time nonlinear stochastic systems and investigate useful recursive procedures for estimating the states of these systems. Since mathematical models derived by engineers are not free from modeling errors in practice, it is an important task to investigate state estimation methods which work well for stochastic systems with unknown disturbances. So, in this paper, we deal with nonlinear stochastic systems with unknown disturbances and investigate state estimation methods which satisfy disturbance decoupling property and can be applicable to nonlinear systems. Numerical simulations are given to show usefulness of the proposed method over the standard extended Kalman filter.

New Filtering Algorithms with Disturbance Decoupling Property for Nonlinear Stochastic Systems with Unknown Disturbances