Statistical characterization of nonhomogeneous and nonstationary backgrounds

Andrew D. Keckler; Dennis L. Stadelman; Donald D. Weiner; Mohamed-Adel Slamani

doi:10.1117/12.276696

20 June 1997 Statistical characterization of nonhomogeneous and nonstationary backgrounds

Andrew D. Keckler, Dennis L. Stadelman, Donald D. Weiner, Mohamed-Adel Slamani

Proceedings Volume 3062, Targets and Backgrounds: Characterization and Representation III; (1997) https://doi.org/10.1117/12.276696
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Andrew D. Keckler, Dennis L. Stadelman, Donald D. Weiner, and Mohamed-Adel Slamani "Statistical characterization of nonhomogeneous and nonstationary backgrounds", Proc. SPIE 3062, Targets and Backgrounds: Characterization and Representation III, (20 June 1997); https://doi.org/10.1117/12.276696

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