Effects of network topology on the conditional distributions of surrogated generalized coherence estimates

Lauren Crider, Douglas Cochran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Coherence estimation is an established approach in multiple-channel detection and estimation, providing optimal solutions in many cases. Recent work has considered the use of maximum-entropy matrix completion when elements are missing from the gram matrix from which the coherence statistics are formed. This is desirable in sensor network settings, for example, where direct communication is not available between every pair of nodes in the network. This paper examines the role of network topology in determining the conditional distributions of the statistic obtained by the matrix completion process under both signal-present and signal-absent hypotheses.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages465-469
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

    Crider, L., & Cochran, D. (2015). Effects of network topology on the conditional distributions of surrogated generalized coherence estimates. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 465-469). [7094486] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094486