Fusion of Correlated Decisions Using Regular Vine Copulas

Shan Zhang, Lakshmi Narasimhan Theagarajan, Sora Choi, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


In this paper, we propose a regular vine copula based methodology for the fusion of correlated decisions. Regular vine copula is an extremely flexible and powerful graphical model to characterize complex dependence among multiple modalities. It can express a multivariate copula by using a cascade of bivariate copulas, the so-called pair copulas. Assuming that local detectors are single threshold binary quantizers and taking complex dependence among sensor decisions into account, we design an optimal fusion rule using a regular vine copula under the Neyman-Pearson framework. In order to reduce the computational complexity resulting from the complex dependence, we propose an efficient and computationally light regular vine copula based optimal fusion algorithm. Numerical experiments are conducted to demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Article number8651356
Pages (from-to)2066-2079
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number8
StatePublished - Apr 15 2019
Externally publishedYes


  • Distributed detection
  • decision fusion
  • dependence modeling
  • regular vine copula
  • sensor fusion

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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