Statistical analysis of 1D HRR target features

David C. Gross; James L. Schmitz; Robert L. Williams

doi:10.1117/12.396373

24 August 2000 Statistical analysis of 1D HRR target features

David C. Gross, James L. Schmitz, Robert L. Williams

Proceedings Volume 4053, Algorithms for Synthetic Aperture Radar Imagery VII; (2000) https://doi.org/10.1117/12.396373
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

David C. Gross, James L. Schmitz, and Robert L. Williams "Statistical analysis of 1D HRR target features", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396373

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