Consensus Estimation via Belief Propagation

Author

Dai, Huaiyu ; Zhang, Yanbing

Author_Institution

NC State Univ., Raleigh

fYear

2007

fDate

14-16 March 2007

Firstpage

277

Lastpage

281

Abstract

In this paper, a new problem, consensus estimation, is formulated, whose setting is complementary to the well-known CEO problem. In particular, a set of nodes are employed to sense and estimate a common source, and the purpose is to reach the best possible estimate for all nodes, through local processing and information exchange over the network. The belief propagation algorithm is adopted to provide a common information processing and dissemination framework for such a purpose. The discussion is also extended to the application of estimating a Markov random field.

Keywords

Gaussian distribution; Markov processes; belief maintenance; distributed algorithms; CEO problem; Gaussian distribution; Markov random field; belief propagation; common information dissemination; common information processing; common source estimation; consensus estimation; information exchange algorithm; sensor networks; Belief propagation; Distributed processing; Gaussian distribution; Information processing; Information technology; Markov random fields; Mobile communication; Pervasive computing; Robustness; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Sciences and Systems, 2007. CISS '07. 41st Annual Conference on

Conference_Location

Baltimore, MD

Print_ISBN

1-4244-1063-3

Electronic_ISBN

1-4244-1037-1

Type

conf

DOI

10.1109/CISS.2007.4298313

Filename

4298313