Adaptive fusion model for distributed detection system

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

3 Scopus citations


There has been a growing interest in developing efficient and reliable distributed detection systems for target recognition and communications. Chair and Varshney have derived an optimal decision rule for fusing decisions based on the Bayesian criterion. To implement the rule, the probability of detection PD and the probability of false alarm PF for each detector must be known, but this information is not always available in practice. This paper presents an adaptive fusion model which estimate the PD and PF adaptively during the decision fusing process. The estimation is implemented by a simple statistical method. That is, the estimates for PD and PF of the ith detector are obtained by counting the number of its decisions that are considered to be correct and incorrect, respectively. Since reference signals are not given, whether the decision of a local detector is considered correct or incorrect is arbitrated by the fused decision of all the other local detectors; that is, the fused decision of all other local detectors is used as the reference for the ith detector. Furthermore, in the work, the fused results of the other local decisions are classified as 'reliable' and 'unreliable'. Only reliable decisions are used to develop the decision rule. Analysis on classifying the fused decisions in terms of reducing estimation error is given, and simulation results which conform to our analysis are presented.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Number of pages10
ISBN (Print)0819410292
StatePublished - 1993
EventSensor Fusion V - Boston, MA, USA
Duration: Nov 15 1992Nov 17 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherSensor Fusion V
CityBoston, MA, USA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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