Distributed extended Kalman filter with nonlinear consensus estimate. Issue 17 (November 2017)

• Record Type: Journal Article
• Authors: Li, Wenling; Jia, Yingmin; Du, Junping
• Is Part Of: Journal of the Franklin Institute. Volume 354:Issue 17(2017)
• Page Start: 7983
• Page End: 7995
• Publication Date: 2017-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2017.09.033
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper is concerned with the distributed filtering problem for discrete-time nonlinear systems over a sensor network. In contrast with the distributed filters with linear consensus estimate, a distributed extended Kalman filter (EKF) is developed with nonlinear consensus estimate. Specifically, a new nonlinear consensus protocol with polynomial form is proposed to generate the consensus estimate. By using the variance-constrained approach, the Kalman gain matrix is determined for each node to guarantee an optimized upper bound on the state estimation error covariance despite consensus terms and linearization errors. It is shown that the Kalman gain matrix can be derived by solving two Riccati-like difference equations. The effectiveness of the proposed filter is evaluated on an indoor localization of a mobile robot with visual tracking systems.