Asymptotic refinements in Bayesian distributed detection

Adrian Papamarcou*, Po-Ning Chen

*Corresponding author for this work

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

Abstract

The performance of a parallel distributed detection system is investigated as the number of sensors tends to infinity. It is assumed that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for Bayesian binary hypothesis testing. Large deviations techniques are employed to show that the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents, but extends to the actual Bayes error probabilities up to a multiplicative constant. This is true as long as the two hypotheses are mutually absolutely continuous; no further assumptions, such as boundedness of second moments of the post-quantization log-likelihood ratio, are needed.

Original languageEnglish
Title of host publicationProceedings of the 1993 IEEE International Symposium on Information Theory
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0780308786
DOIs
StatePublished - 1 Jan 1993
EventProceedings of the 1993 IEEE International Symposium on Information Theory - San Antonio, TX, USA
Duration: 17 Jan 199322 Jan 1993

Publication series

NameProceedings of the 1993 IEEE International Symposium on Information Theory

Conference

ConferenceProceedings of the 1993 IEEE International Symposium on Information Theory
CitySan Antonio, TX, USA
Period17/01/9322/01/93

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