Asymptotic performance of categorical decision making with random thresholds

Thakshila Wimalajeewa, Pramod K. Varshney

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

In this letter, we investigate the asymptotic performance of categorical decision fusion in a human decision making framework. We assume that multiple human agents send categorized information to a moderator for final decision making. The local categorization is performed via a threshold based scheme where thresholds are assumed to be random variables. Considering the cases where the moderator has the knowledge of exact threshold values as well as when it has only probabilistic information of the individual thresholds, we analyze the asymptotic performance of likelihood ratio based decision fusion at the moderator in terms of the Chernoff information. Numerical results are presented for illustration.

Original languageEnglish (US)
Article number6809962
Pages (from-to)994-997
Number of pages4
JournalIEEE Signal Processing Letters
Volume21
Issue number8
DOIs
StatePublished - Aug 2014

Keywords

  • Asymptotic performance
  • Chernoff information
  • decision fusion
  • human decision making
  • likelihood ratio test

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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