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Proceedings Paper

Statistical characterization of nonhomogeneous and nonstationary backgrounds
Author(s): Andrew D. Keckler; Dennis L. Stadelman; Donald D. Weiner; Mohamed-Adel Slamani
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Paper Abstract

The statistical characterization of complex real-world backgrounds is a crucial issue in the design of effective detection algorithms. The approach taken here is to monitor the environment and divide it into homogeneous partitions which are characterized by their probability distributions. A new technique for characterizing multivariate random data is described and the effectiveness of the approach is illustrated by two applications: concealed weapon detection and weak signal detection in strong non-Gaussian clutter.

Paper Details

Date Published: 20 June 1997
PDF: 10 pages
Proc. SPIE 3062, Targets and Backgrounds: Characterization and Representation III, (20 June 1997); doi: 10.1117/12.276696
Show Author Affiliations
Andrew D. Keckler, Syracuse Univ. (United States)
Dennis L. Stadelman, Syracuse Univ. (United States)
Donald D. Weiner, Syracuse Univ. (United States)
Mohamed-Adel Slamani, Stiefvater Consultants (United States)


Published in SPIE Proceedings Vol. 3062:
Targets and Backgrounds: Characterization and Representation III
Wendell R. Watkins; Dieter Clement, Editor(s)

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