State estimation for discrete-time sensor networks with event-triggered sampling

This paper investigates state estimation problem for target plant described by discrete-time systems with event-triggered sampling. Through partial output measurements obtained in an event-triggered sampling method from every sensor, we are aiming to design a state estimator to approximate states of the target plant, which reduces the communication cost between the state estimator and the sensor networks. By applying a input-to-state stable Lyapunov function and linear matrix inequalities, a sufficient condition are presented to guarantee convergence of estimation error systems. Finally a numerical example is given to verify effectiveness of the proposed theoretical results.