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Proceedings Paper

Synthetic aperture radar hybrid ATR system
Author(s): Andrew Hauter; Kuo-Chu Chang
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Paper Abstract

A hybrid automatic target recognition system is presented that exploits advances in two new fields in detection theory and signal analysis. The first is in the area of Universal Classification that offers asymptotic optimal solutions to non-Gaussian properties of signals and the second is in the field of multi-resolution analysis (MRA) that uses the automatic feature isolating properties of the wavelet transform. The Universal Classifier is used as the first stage of a hybrid ATR system that efficiently shifts through large quantities of imagery locating regions of interest that contain `target-like' features. The target chips of interest are then passed through the MRA to be classified at the final stage. Wavelets are adequate to the study of unpredictable signals with both low frequency components and sharp transitions. As a result, there has been recent interest in applying this new signal processing field to the target recognition problem. But few have combined the natural feature extraction capability of time- frequency methods in the classification stage. In this approach, we utilize the sub-space `crystals' from a specific decomposition and operate a classification strategy against each crystal of the transform. The complete ATR system is presented as well as performance examples using both real synthetic aperture radar data and data generated using the Xpatch signature prediction code.

Paper Details

Date Published: 24 May 1996
PDF: 8 pages
Proc. SPIE 2756, Automatic Object Recognition VI, (24 May 1996); doi: 10.1117/12.241144
Show Author Affiliations
Andrew Hauter, George Mason Univ. (United States)
Kuo-Chu Chang, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 2756:
Automatic Object Recognition VI
Firooz A. Sadjadi, Editor(s)

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