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Proceedings Paper

Parametric model-based characterization of IR clutter
Author(s): John D. McGlynn; Dino J. Sofianos
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Paper Abstract

This paper describes a method for parametrically characterizing IR clutter for missile seeker applications in terms of a Butterworth model of the power spectral density (PSD). Traditionally, models of the PSD have been characterized by a Gauss-Markov correlation model, or a similar variant. These tend to estimate the overall spectral shape and integrated spectral power (rms) level very well. However, they tend to be dominated by large spatial features, i.e., low wavenumbers, and consequently, tend to underestimate the power present at high wavenumbers. While this is often not a factor in many remote sensing applications, it is a critical issue for missile seekers, as targets are often sub pixel in extent, and hence compete against clutter at high spatial frequencies. The clutter parameterization algorithm performs a fit to a one-dimensional profile taken from a radially average slice through a two-dimensional PSD computed from geometrically and radiometrically corrected airborne dual-band IR imagery. The parameterization is iteratively constrained to optimize the fit at high wavenumbers, and a two-dimensional isotropic model is computed. A description of the parameterization algorithm, as well as a synopsis of model fits to various clutter types are presented. Additionally, a table of recommended global PSD parameters for clutter characterization (by waveband) is presented herein.

Paper Details

Date Published: 22 May 1995
PDF: 9 pages
Proc. SPIE 2470, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VI, (22 May 1995); doi: 10.1117/12.210064
Show Author Affiliations
John D. McGlynn, Science Applications International Corp. (United States)
Dino J. Sofianos, Science Applications International Corp. (United States)


Published in SPIE Proceedings Vol. 2470:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VI
Gerald C. Holst, Editor(s)

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