Research On State Estimation In Unreliable Communication Networks

Networks have been widely utilized in daily lives due to development of science and technology.Networks have the advantage of long transmission distance,reliable communication and anti-interference.It has great practical significance that using networks to replace traditional transmission modes in industry.Therefore,networked control has been wildely studied and applied in many practical insdustrial systems.State estimation is an important part of control theories.Nowdays,most of control methods are based on state feedback.If system states can not be obtained,then it is very difficult to control the system.Therefore,state estimation is a very important issue in studies of networked control.In this paper,the following aspects have been studied based on Kalman filtering theories for unreliable networked systems with delay and packets dropout.Firstly,a Kalman filtering problem has been stuied for an unreliable networked system subject to Markovian packets dropout.A nonuniform sampling strategy has been proposed to save resourses of both sampler and networks.A nonuniform sampling Kalman filtering algorithm has been designed when networked systems are subject to Makrovian packets dropout.Furthermore,convergence of estimation errors and boundness of error covariance have been proven for the proposed filtering algorithm.Secondly,a multi-sensor state estimation problem has been studied for an unreliable multi-sensor networked system subject to a constant delay and Bernoulli packets dropout.A hierarchical fusion strategy has been designed to improve estimation performance of the multi-sensor networked system in the case of unreliable communication.Based on the hierarchical fusion strategy,a fusion algorithm has been designed to improve the estimation performance for the multi-sensor networked system.Finally,a distributed Kalman filtering alrogirhm is designed for sensor networks subject to time-varying tranmssion delays.The designed algorithm can estimate state accurately for sensor networks with delays.And convergence of estimation errors and boundness of error covariance are proven based on the designed algorithm,respectively.