State Estimation for Nonlinear Discrete-Time Systems with Markov Jumps and Nonhomogeneous Transition Probabilities

Abstract EN

State estimation problem is addressed for a class of nonlinear discrete-time systems with Markov parameters and nonhomogeneous transition probabilities (TPs).

In this paper, the optimal estimation mechanism of transition probability matrix is proposed in the minimum mean square error sense to show some critical points.

Based on this mechanism, the extended Kalman filters are employed as the subfilters to obtain the subestimates with corresponding models.

A novel operator which fuses the prior knowledge and the posterior information embedded in observations is developed to modify the posterior mode probabilities.

A meaningful example is presented to illustrate the effectiveness of our method.