Fusing heterogeneous data for detection under non-stationary dependence

Hao He, Arun Subramanian, Pramod K. Varshney, Thyagaraju Damarla

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

8 Scopus citations

Abstract

In this paper, we consider the problem of detection for dependent, non-stationary signals where the non-stationarity is encoded in the dependence structure. We employ copula theory, which allows for a general parametric characterization of the joint distribution of sensor observations and, hence, allows for a more general description of inter-sensor dependence. We design a copula-based detector using the Neyman-Pearson framework. Our approach involves a sample-wise copula selection scheme, which for a simple hypothesis test, is proved to perform better than previously used single copula selection schemes. We demonstrate the utility of our copula-based approach on simulated data, and also for outdoor sensor data collected by the Army Research Laboratory at the US southwest border.

Original languageEnglish (US)
Title of host publication15th International Conference on Information Fusion, FUSION 2012
Pages1792-1799
Number of pages8
StatePublished - Oct 24 2012
Event15th International Conference on Information Fusion, FUSION 2012 - Singapore, Singapore
Duration: Sep 7 2012Sep 12 2012

Publication series

Name15th International Conference on Information Fusion, FUSION 2012

Other

Other15th International Conference on Information Fusion, FUSION 2012
CountrySingapore
CitySingapore
Period9/7/129/12/12

Keywords

  • Detection
  • dependence modeling
  • heterogeneous sensing
  • information fusion
  • model selection
  • sensor fusion

ASJC Scopus subject areas

  • Information Systems

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