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Discrete Dynamics in Nature and Society
Volume 2015 (2015), Article ID 576341, 9 pages
Research Article

Consensus Information Filtering for Large-Scale Systems with Application to Heat Conduction Process

1School of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China
2Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China
3School of Science, Communication University of China, Beijing 100024, China

Received 20 October 2015; Revised 9 November 2015; Accepted 17 November 2015

Academic Editor: Deqing Huang

Copyright © 2015 Liguo Zhang and Ying Lyu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For large-scale distributed systems, the evolution of system dynamics is dominated by current states and boundary conditions simultaneously. This work describes a distributed consensus filtering for a class of large-scale distributed systems with unknown boundary conditions, which are monitored by a set of sensors. Because of the difference of spatial positions among the sensor network, only the single state variables or both the states and outside input jointly could be estimates with Kalman information filtering, respectively. On diffusion processing, we fuse the common state estimations of the local information filters using consensus averaging algorithms and algebraic graph theory. Stability and performance analysis is provided for this distributed filtering algorithm. Finally, we consider an application of distributed estimation to a heat conduction process. The performance of the proposed distributed algorithm is compared to the centralized Kalman filtering.