Fusion of Correlated Decisions Using Regular Vine Copulas

TL;DR

This paper introduces a novel fusion method for correlated decisions using regular vine copulas, effectively modeling complex dependencies among sensor decisions to improve detection performance.

Contribution

It presents a new regular vine copula-based framework for optimal decision fusion that accounts for complex dependencies and reduces computational complexity.

Findings

Effective modeling of complex sensor decision dependencies

Improved detection performance demonstrated in experiments

Reduced computational complexity of fusion process

Abstract

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.