Consensus Estimation via Belief Propagation

Huaiyu Dai, Yanbing Zhang
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xmt3q74jnnf7nlg25xkjze2uoa" style="color: black;">2007 41st Annual Conference on Information Sciences and Systems</a> </i> &nbsp;
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
more &raquo; ... s also extended to the application of estimating a Markov random field. I. INTRODUCTION Recent advances in information technology are leading to a paradigm shift towards ad hoc networking, distributed processing and pervasive computing and communications. Examples include mobile ad hoc networks, wireless mesh networks, and sensor networks for various military, commercial, environmental and emergency applications. The well-known CEO problem [1][2] [3] in a decentralized communication setting is formulated as follows. A team of agents are deployed to observe a common source and report to the CEO (chief executive officer or central estimation officer) independently (i.e., they are not allowed to convene) with a sum rate constraint. The CEO reconstructs the source based on these reports, and the goal is to achieve the best distortion-rate tradeoff.
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