Sequential Fusion Estimation For Uncertain Networked Systems Based On Event Triggerin

With the rapid development of information technology in today’s society,networked systems have been playing a vital role in all areas of human life.However,due to the limitation of network bandwidth,various problems occurre during the transmission of data,including the fading observation,the packet loss and so on.The application of the non-periodic sampling event-triggered mechanisms can effectively reduce the transmission pressure,save the resource cost and extend the system life,moreover the utilization of sequential fusion method can effectively reduce the computational burden at the fusion center.Therefore,the paper selectes the eligible information to transmit in the mixed uncertain networked system,which achieves the purpose of saving network communication energy,and uses Sequential Covariance Intersection(SCI)and Sequential Inverse Covariance Intersection(SICI)fusion estimation algorithms to perform the state fusion filtering separately,which greatly reduced the computational burden and improved the overall estimation accuracy of the system.For the various network induced phenomena occurred in sensor networked system,the optimal distributed,SCI and SICI fusion estimators are proposed for the multiplicative noises networked systems with uncorrelated or correlated noises,respectively,using the designed observation-based event-triggered mechanism.Using the designed innovation-based event-triggered mechanism,the optimal sequential,SCI and SICI fusion estimation algorithms are proposed for the networked systems with the fading observations,and the more complex networked systems with fading observations,multiplicative noises and correlated noises,respectively.By the designed innovation-based adaptive event-triggered mechanism,robust SCI and SICI fusion estimation algorithms are proposed for the uncertain networked systems with uncertain noise variances,fading observations,multiplicative noises,and correlated noises,and the robustnesses of those algorithms are proved.The correctness and effectiveness of each of the proposed algorithms are verified by simulation experiments.