EVIDENTIAL REASONING APPROACH TO DISTRIBUTED MULTIPLE-HYPOTHESIS DETECTION.

J. J. Chao*, E. Drakopoulos, Chung Chieh Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

A distributed detection system consisting of n local detectors and a fusion center is considered. The local detectors send their inferences with regard to a set of M candidate hypotheses to the fusion center where the final decision is made. Unlike conventional detectors which produce single-hypothesis hard decisions, each local detector produces a 'subset' of the M hypotheses and a discrete confidence level associated with that subset. The fusion center then employs A. P. Dempster's (1986) combining rule for evidence aggregation. The optimum confidence-based partitioning of local decision space with respect to the system performance is studied. For the M equals 3 case, it is shown that the presented system greatly outperforms the one in which each local detector provides a single-hypothesis hard decision.

Original languageEnglish (US)
Pages (from-to)1826-1831
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - Dec 1 1987

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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