A fuzzy modeling approach to decision fusion under uncertainty

V. N S Samarasooriya, Pramod Kumar Varshney

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

A multi-sensor decision fusion scheme is presented in which the probabilities associated with the local sensor decisions are known to vary in a nonrandom fashion around their design values. The uncertainties associated with the local decisions are modeled by means of fuzzy sets. A Bayesian approach is used to design the optimum fusion rule for the case where the local sensor decisions are statistically independent across the sensors. In order to reach a crisp decision, the global Bayesian risk is defuzzified using a criterion for mapping fuzzy sets on to the real line. The performance of the optimum fusion rule obtained is illustrated by means of a numerical example.

Original languageEnglish (US)
Pages (from-to)59-69
Number of pages11
JournalFuzzy Sets and Systems
Volume114
Issue number1
StatePublished - Aug 16 2000

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Keywords

  • Bayes criterion
  • Decision fusion
  • Fuzzy sets
  • Multi-sensor systems
  • Total distance criterion

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Statistics and Probability

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