Distributed finite time cubature information filtering with unknown correlated measurement noises. (June 2021)

• Record Type: Journal Article
• Authors: Liang, Yuan; Dong, Xiwang; Wang, Hong; Han, Liang; Li, Qingdong; Ren, Zhang
• Is Part Of: ISA transactions. Volume 112(2021)
• Page Start: 35
• Page End: 55
• Publication Date: 2021-06
• Subjects: Distributed state estimation -- Sensor network -- Cubature information filtering -- Unknown correlated measurement noises -- Finite communication time -- Stability; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.12.011
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper addresses the distributed state estimation problem for a class of discrete nonlinear system over sensor networks subject to unknown correlated measurement noises. Firstly, under the condition of network connectivity, a novel communication protocol is developed to ensure every sensor node can gather the information distributed throughout the network within finite communication time. Then a fully distributed estimator is designed by periodically fusing the local information and neighbor's information according to the covariance intersection fusion strategy. Theoretically, it is proved that the distributed estimator in each sensor node is stable with the exponentially bounded estimation error in mean square. Finally, some numerous simulations are performed to illustrate the practical effectiveness and superiority of the proposed state estimator. Graphical abstract: Highlights: Design a novel finite time communication protocol, based on which every node could collect all the information distributed throughout the network within finite communication time under network connectivity. Develop a new distributed state estimator for a class of discrete nonlinear system with unknown correlated measurement noises. Establish sufficient conditions for guaranteeing the exponential stability of the new estimator in mean square sense.