Optimal local detection for sensor fusion by large deviation analysis

Dongliang Duan, Liuqing Yang, Louis L. Scharf

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

2 Scopus citations

Abstract

Fusion is widely used to improve the overall detection performance in applications such as radar, wireless sensor networks, wireless communications, spectrum sensing and so on. While the optimum fusion strategy for any preset local decision performance can be easily obtained by the Neyman-Pearson lemma, the selection of the local detection strategy that optimizes the global performance is intractable due to its complexity and the limited global information at local detectors. In this paper, we use large deviation analysis to determine a local decision rule to optimize the asymptotic global performance. Some interesting properties of the decision rule are observed. Numerical results show that our proposed strategy approximates the optimal performance very well even with a small number of local detectors.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Pages744-748
Number of pages5
StatePublished - 2012
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: Aug 27 2012Aug 31 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
CountryRomania
CityBucharest
Period8/27/128/31/12

Keywords

  • asymptotic performance
  • global performance
  • large deviation analysis
  • optimal local detection strategy
  • sensor fusion

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