Decentralized signal detection with fuzzy information

Vajira N.S. Samarasooriya, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We extend the decentralized signal detection problem to the case where the observations available at the sensors display measurement inaccuracy. The imprecise measurements at each sensor are modeled as fuzzy information systems. The fuzzy information system model consists of fuzzy partitions defined on the corresponding crisp (nonfuzzy) observation space. A Bayesian decision criterion is employed in the design of the signal detection schemes at the local sensors and the decision fusion center. Compression of the input fuzzy systems at the sensors into a fewer number of levels is studied. Both optimal and suboptimal methods are employed to partition the fuzzy observation space for signal detection applications.

Original languageEnglish (US)
Pages (from-to)658-668
Number of pages11
JournalOptical Engineering
Volume36
Issue number3
DOIs
StatePublished - Mar 1997

Keywords

  • Bayes criterion
  • Data compression
  • Data fusion
  • Decentralized detection
  • Fuzzy information systems
  • Multisensor systems
  • Sensor fusion

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

Fingerprint Dive into the research topics of 'Decentralized signal detection with fuzzy information'. Together they form a unique fingerprint.

Cite this