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Proceedings Paper

WAVENET feature extraction of high-range resolution radar profiles for automatic target recognition
Author(s): Hedley C. Morris; Monica M. De Pass
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Paper Abstract

We propose a WAVENET method for feature extraction of high-range resolution (HRR) radar profiles. Because HRR signals constantly vary with incremental changes in time and target aspect, the inverse problem we address is that of extracting a subset of discriminatory features from a set of HRR profiles that are unique to each target class. Based on, we construct a neural net technique built on wavelets for determining the discriminating features separating each target class. The method involves choosing a suitable set of child wavelets, such that the transformation of the original data (the training set of HRR profiles) will enhance the nonlinear separability of different classes of target signals while significantly reducing the dimension of the data.

Paper Details

Date Published: 28 March 2005
PDF: 10 pages
Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); doi: 10.1117/12.603976
Show Author Affiliations
Hedley C. Morris, San Jose State Univ. (United States)
Monica M. De Pass, Claremont Graduate Univ. (United States)


Published in SPIE Proceedings Vol. 5818:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III
Harold H. Szu, Editor(s)

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