Fusion for the detection of dependent signals using multivariate copulas

Arun Subramanian, Ashok Sundaresan, Pramod K. Varshney

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

11 Scopus citations

Abstract

The use of multimodal or heterogeneous sensors for surveillance greatly increases the diversity of information available from a given region of interest. Since the underlying scene is the same for all the sensors, the data across the sensors are inherently dependent. The nature of this dependence can be quite complex and quantifying it is a challenging task, especially in the case of heterogeneous sensing. We consider the problem of fusion for the detection of dependent, heterogeneous signals and design a detector using a copula-based framework. Past applications using the copula based approach have mostly been limited to the bivariate (2 sensor) case. We will address copula construction and model selection issues for the multivariate case.

Original languageEnglish (US)
Title of host publicationFusion 2011 - 14th International Conference on Information Fusion
StatePublished - Sep 13 2011
Event14th International Conference on Information Fusion, Fusion 2011 - Chicago, IL, United States
Duration: Jul 5 2011Jul 8 2011

Publication series

NameFusion 2011 - 14th International Conference on Information Fusion

Other

Other14th International Conference on Information Fusion, Fusion 2011
CountryUnited States
CityChicago, IL
Period7/5/117/8/11

Keywords

  • Dependence modeling
  • Detection
  • Heterogeneous sensing
  • Information fusion
  • Model selection
  • Sensor fusion

ASJC Scopus subject areas

  • Information Systems

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    Subramanian, A., Sundaresan, A., & Varshney, P. K. (2011). Fusion for the detection of dependent signals using multivariate copulas. In Fusion 2011 - 14th International Conference on Information Fusion [5977720] (Fusion 2011 - 14th International Conference on Information Fusion).