Abstract

Computational issues affecting the feasibility of optimal distributed detection with correlated measurements are well recognized. We propose utilizing the t-cherry junction tree, an approach based on probabilistic graphical models, to approximate the joint distribution of the correlated measurements. In principle, this approach provides a sequence of progressively more efficiently represented approximations that enable tradeoff between fidelity and compactness. Practically, however, the impact of generating estimated distributions from training data can be significant as the number of parameters to estimate in a distribution grows exponentially with the number of random variables in the distribution. This limitation is quantified and the performance of this approach is illustrated via simulation studies.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1199-1203
Number of pages5
ISBN (Electronic)9781479982974
DOIs
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Other

Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/2/1411/5/14

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Proulx, B., Zhang, J., & Cochran, D. (2015). Enabling distributed detection with dependent sensors. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 1199-1203). [7094648] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094648