Distributed consensus extended Kalman filter: a variance‐constrained approach. Issue 3 (1st February 2017)

• Record Type: Journal Article
• Authors: Li, Wenling; Jia, Yingmin; Du, Junping
• Is Part Of: IET control theory & applications. Volume 11:Issue 3(2017)
• Page Start: 382
• Page End: 389
• Publication Date: 2017-02-01
• Subjects: Kalman filters -- nonlinear filters -- state estimation -- covariance analysis -- matrix algebra -- Riccati equations -- difference equations -- nonlinear control systems; distributed consensus extended Kalman filter -- distributed EKF -- variance‐constrained approach -- distributed state estimation problem -- nonlinear systems -- sensor networks -- state estimation error covariance -- Kalman gain matrix -- Riccati‐like difference equations -- mobile robot indoor localisation -- visual tracking systems; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2016.1054
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : This study is concerned with the distributed state estimation problem for non‐linear systems over sensor networks. By using the strategy of consensus on prior estimates, a distributed extended Kalman filter (EKF) is developed for each node to guarantee an optimised upper bound on the state estimation error covariance despite consensus terms and linearisation errors. The Kalman gain matrix is derived for each node by solving two Riccati‐like difference equations. It is shown that the estimation error is bounded in mean square under certain conditions. The effectiveness of the proposed filter is evaluated on an indoor localisation of a mobile robot with visual tracking systems.