Integration of multiple adaptive algorithms for parallel decision fusion

Weiqiang Dong, Moshe Kam

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

1 Scopus citations

Abstract

The Chair-Varshney rule for parallel binary decision fusion requires knowledge of the a priori probabilities of the hypotheses and the performance of the sensors (probabilities of false alarm and missed detection). In most applications, this information is not available. Five methods were developed so far for estimating the unknown probabilities. However, none of them is the best under all circumstances. We present an algorithm that selects the best of these five methods. The algorithm estimates roughly the value of the a priori probabilities and the sensor performance from input data, and seeks support from a data base that provides archival data from the five methods at this operating point. In simulation, the algorithm performed on average better than each one of the five existing methods operating alone.

Original languageEnglish (US)
Title of host publication2016 50th Annual Conference on Information Systems and Sciences, CISS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages355-359
Number of pages5
ISBN (Electronic)9781467394574
DOIs
StatePublished - Apr 26 2016
Event50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States
Duration: Mar 16 2016Mar 18 2016

Publication series

Name2016 50th Annual Conference on Information Systems and Sciences, CISS 2016

Other

Other50th Annual Conference on Information Systems and Sciences, CISS 2016
CountryUnited States
CityPrinceton
Period3/16/163/18/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

Keywords

  • Adaptive Decision Fusion
  • Data Fusion
  • Decision Fusion
  • Distributed Detection
  • Probability Estimation

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