Fuzzy modeling approach to decision fusion under uncertainty

V. N S Samarasooriya, Pramod Kumar Varshney

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

5 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)
Title of host publicationIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Editors Anon
PublisherIEEE Computer Society
Pages788-795
Number of pages8
StatePublished - 1996
EventProceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems - Washington, DC, USA
Duration: Dec 8 1996Dec 11 1996

Other

OtherProceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems
CityWashington, DC, USA
Period12/8/9612/11/96

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

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    Samarasooriya, V. N. S., & Varshney, P. K. (1996). Fuzzy modeling approach to decision fusion under uncertainty. In Anon (Ed.), IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 788-795). IEEE Computer Society.