Resilient filtering for time-varying stochastic coupling networks under the event-triggering scheduling

Abstract

The resilient filtering problem is considered for a class of time-varying networks with stochastic coupling strengths. An event-triggered strategy is adopted to save the network resources by scheduling the signal transmission from the sensors to the filters based on certain prescribed rules. Moreover, the filter parameters to be designed are subject to gain perturbations. The primary aim of the addressed problem is to determine a resilient filter that ensures an acceptable filtering performance for the considered network with event-triggering scheduling. To handle such an issue, an upper bound on the estimation error variance is established for each node according to the stochastic analysis. Subsequently, the resilient filter is designed by locally minimizing the derived upper bound at each iteration. Moreover, rigorous analysis shows the monotonicity of the minimal upper bound regarding the triggering threshold. Finally, a simulation example is presented to show effectiveness of the established filter scheme.

Keywords:

• Resilient filtering
• time-varying networks
• stochastic coupling strengths
• event-triggered mechanism
• recursive method

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 61673110], the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence [grant number BM2017002], the Six Talent Peaks Project for the High Level Personnel from the Jiangsu Province of China [grant number 2015-DZXX-003], the Scientific Research Foundation of Graduate School of Southeast University [grant number YBJJ1616] and the Graduate Innovation Program of Jiangsu Province [grant number KYLX15_0105].