The optimality of a class of distributed estimation fusion algorithm

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

16 Scopus citations

Abstract

When the measurement noises across sensors at the same time may be correlated, for linear minimum mean-squared errors (LMMSE) estimation, a systematic way to handle the corresponding distributed estimation fusion problem is proposed in this paper based on a unified data model for linear unbiased estimation. The optimality (equivalence to the optimal centralized estimation fusion) of the new optimal distributed estimation fusion algorithm is then analyzed. A necessary and sufficient condition of the optimality for the general case and sufficient conditions for two special cases are given. Comparisons with the existing distributed estimation fusion algorithms are also discussed.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Information Fusion, FUSION 2008
DOIs
StatePublished - 2008
Externally publishedYes
Event11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany
Duration: 30 Jun 20083 Jul 2008

Publication series

NameProceedings of the 11th International Conference on Information Fusion, FUSION 2008

Conference

Conference11th International Conference on Information Fusion, FUSION 2008
Country/TerritoryGermany
CityCologne
Period30/06/083/07/08

Keywords

  • Centralized fusion
  • Cross correlation
  • Distributed fusion
  • Estimation fusion
  • Linear minimum mean-squared errors

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