Event-triggered state estimator design for unknown input and noise-correlated random system

In this study, an event-driven state estimator is designed for stochastic systems that contain unknown inputs and processes as well as correlated measurement noise. First, the event-triggered state estimator's gain is deduced by using the random stability theory and Lyapunov's function. Then, based on the results, the corresponding state estimation errors are calculated in mean square convergence. Second, the corresponding unknown inputs are inhibited by using output errors of the estimator. In addition, the corresponding event-driven transmission strategy is designed by using a quadratic performance index, which guarantees a good balance between the estimation error and the data transmission rate as well as prolonged service life of the sensor battery. Finally, numerical simulation tests verify that the designed event-driven state estimator can estimate the system's state effectively and extend the sensor's battery life by approximately 48%. The proposed algorithm also leads to reduced utilization of network resources to some degree.