Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Steven C. Gustafson, PhD


Noncooperative identification (NCTI) of airborne targets is a top priority for the Air Force and this thesis makes a significant contribution to that area. High range resolution radar (HRR) provides an attractive means to perform NCTI. When measured HRR data is used, classification performance is excellent. However, it is often not feasible to acquire measured HRR signatures for a wide set of targets, thus necessitating the use of synthetically generated HRR data. Classification performance suffers severe degradation when using synthetic data. This thesis suggests that a large portion of HRR signature content is non-discriminatory and that this content is a cause of classifier degradation for the case of synthetic data. A unique wavelet-based denoising methodology is developed which is optimized with respect to classification accuracy. In the case of synthetic data, the denoising method leads to remarkable classification improvements. Classification accuracies are obtained which are comparable to those when training on measured data. This is an unprecedented result. It is also shown that the denoising approach of this thesis leads to superior classification results compared to those obtained with traditional wavelet-based methods.

AFIT Designator


DTIC Accession Number



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