Open AccessDistributed consensus filtering for jump Markov linear systems
Author(s) -
Li Wenling,
Jia Yingmin,
Du Junping,
Zhang Jun
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2012.0742
Subject(s) - control theory (sociology) , jump , computer science , consensus , markov chain , markov process , mathematics , multi agent system , artificial intelligence , control (management) , physics , machine learning , statistics , quantum mechanics
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.