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Proceedings Paper

Statistical analysis of 1D HRR target features
Author(s): David C. Gross; James L. Schmitz; Robert L. Williams
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Paper Abstract

Automatic target recognition (ATR) and feature-aided tracking (FAT) algorithms that use one-dimensional (1-D) high range resolution (HRR) profiles require unique or distinguishable target features. This paper explores the use of statistical measures to quantify the separability and stability of ground target features found in HRR profiles. Measures of stability, such as the mean and variance, can be used to determine the stability of a target feature as a function of the target aspect and elevation angle. Statistical measures of feature predictability and separability, such as the Fisher and Bhattacharyya measures, demonstrate the capability to adequately predict the desired target feature over a specified aspect angular region. These statistical measures for separability and stability are explained in detail and their usefulness is demonstrated with measured HRR data.

Paper Details

Date Published: 24 August 2000
PDF: 8 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396373
Show Author Affiliations
David C. Gross, Veridian Engineering (United States)
James L. Schmitz, Veridian Engineering (United States)
Robert L. Williams, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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