Stochastic Event-Triggered Filtering for Nonlinear Systems Subject to Correlated Noises

In this paper, the event-triggered filtering problem is investigated for the nonlinear systems subject to correlated noises. The stochastic event-triggered communication strategy is adopted herein to avert the frequent information exchange and hence mitigate the network communication burden while retaining the Gaussianity of innovation process. In order to handle the correlated characteristic of the process and the measurement noises, an alternative assumption that the two-step predicted estimate satisfies Gaussian distribution is made. In the simulation, the usefulness of the developed algorithms is illustrated by two examples.