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Distributed estimation in the presence of correlated noises

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We propose a method for distributed sequential estimation in the presence of correlated noises. At each time slot, a node exchanges information with its neighbors and then updates the estimate by using the received information from its neighbors and its local observation. It is assumed that the noises have the Markov property with respect to the network topology. A doubly stochastic matrix for combining information from the nodes is employed to average the sufficient statistics over the network. We show that the performance of the proposed method converges to that of the centralized optimal estimator as the iterations go on. Therefore, our algorithm approaches the Cramér-Rao bound asymptotically.

Original languageEnglish
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: Sep 9 2013Sep 13 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference2013 21st European Signal Processing Conference, EUSIPCO 2013
Country/TerritoryMorocco
CityMarrakech
Period09/9/1309/13/13

Keywords

  • Correlated noises
  • distributed estimation
  • least squares estimator
  • sequential estimation

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