Journal
IET CONTROL THEORY AND APPLICATIONS
Volume 7, Issue 12, Pages 1659-1664Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-cta.2012.0742
Keywords
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
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Funding
- National Basic Research Programme of China (973 Programme) [2012CB821200, 2012CB821201]
- NSFC [61134005, 60921001, 61203044, 61104011, 90916024, 91116016]
- Beijing Natural Science Foundation [4132040, 4122046]
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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.
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