Distributed consensus filtering for jump Markov linear systems. Issue 12 (1st August 2013)

• Record Type: Journal Article
• Authors: Li, Wenling; Jia, Yingmin; Du, Junping; Zhang, Jun
• Is Part Of: IET control theory & applications. Volume 7:Issue 12(2013)
• Page Start: 1659
• Page End: 1664
• Publication Date: 2013-08-01
• Subjects: estimation theory -- Kalman filters -- linear systems -- Markov processes -- probability -- target tracking; jump Markov linear system -- distributed consensus filter -- model approach -- mode conditioned estimation -- Kalman consensus filter -- linear minimum variance -- mode probability -- manoeuvring target tracking; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2012.0742
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : This article studies the problem of distributed filtering for jump Markov linear systems in a not fully connected sensor network. A distributed consensus filter is developed by applying an improved interacting multiple model approach in which the mode‐conditioned estimates are derived by the Kalman consensus filter and the mode probabilities are obtained in the sense of linear minimum variance. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm for tracking a manoeuvring target in a sensor work with eight nodes.