Distributed detection with censoring sensors under dependent observations

Research output: Chapter in Book/Entry/PoemConference contribution

7 Scopus citations

Abstract

Distributed detection in censoring sensor networks, where each sensor transmits 'informative' observations to the Fusion Center (FC), and censors those deemed 'uninformative', has been investigated by many researchers, but under the assumption of conditionally independent observations. In this paper, we consider a more realistic situation in a censoring sensor network where observations may not be independent. We derive optimal fusion rules at the FC under both Neyman-Perason (NP) and Bayesian frameworks, assuming that each sensor sends complete observations to the FC only when its observation falls out of a certain no-send region. Simulation results are provided to demonstrate the superior performance of our fusion rule compared with several other fusion rules derived in earlier work.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5055-5059
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Country/TerritoryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Censoring
  • Dependent observations
  • Distributed detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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