State Estimation In Wireless Sensor Network With Event-triggered Communication For Discrete-time Systems

Nowadays，information is everywhere in the modern society and the urgent need pushes thedevelopment of sensors. Wireless sensor networks come into being with the trend. State estimationhas been an important application in the wireless sensor networks and an interesting research topicin a variety of sciences.The communication radius is constrained due to miniaturization of the sensor size. Theperiodic data transmissions produce heavy network traffic, which makes the limited existingcommunication resources more strained. What’s more, data dropouts and transmission delay isinevitable. To reduce the heavy data transmissions，state estimations for the discrete-time systems isinvestigated through introducing event-triggered communication in this paper.The main contributions of this paper can be summarized as follows:In centralized wireless sensor network, estimators are designed to estimate the states of lineardiscrete-time systems by introducing centralized event-triggered communication. Sufficientconditions of the stability of the estimators and event-triggered scenarios are given in the form ofLinear Matrix Inequalities (LMIs) using Input-State Stable (ISS) stability theory. Then, numericalsimulations are made to demonstrate the effectiveness of the estimators.In the distributed sensor networks, distributed estimators are designed by introducingdistributed event-triggered communication. Sufficient conditions of the stability of the estimatorsand event-triggered scenarios are given in the form of Linear Matrix Inequalities (LMIs) usingLyapunov stability theory. Numerical simulations are made to demonstrate the effectiveness of theestimators.The phenomenon of randomly occurring sensor saturations (ROSS) is considered.Bernoulli-distributed sequences with known conditional probabilities are introduced to describe theROSS. In the wireless networks with randomly occurring sensor saturations, distributed estimatorsare designed based on measurements collected from the wireless sensor networks with ROSS.Sufficient conditions of the stability of the estimators and event-triggered scenarios are given in theform of Linear Matrix Inequalities (LMIs) using Lyapunov mean-square stability theory. Numericalsimulations demonstrate the effectiveness of the estimators.State estimations for the discrete-time target system with nonlinearities are investigated.Distributed estimators are designed for the system with nonlinearities and randomly occurring sensor saturations(ROSS) introducing distributed event-triggered communication. Sufficientconditions of the stability of the estimators and event-triggered scenarios are given in the form ofLinear Matrix Inequalities (LMIs) using Lyapunov mean-square stability theory. At last, numericalsimulations demonstrate the effectiveness of the estimators.