Publication Date
2024
Document Type
Thesis
Committee Members
Michael A. Saville, Ph.D. (Advisor); Cheryl B. Schrader, Ph.D. (Committee Member); Michael L. Raymer, Ph.D. (Committee Member); Josh Ash, Ph.D. (Committee Member)
Degree Name
Master of Science in Electrical Engineering (MSEE)
Abstract
Performing automatic target recognition (ATR) on full-size aircraft targets using inverse synthetic aperture radar (ISAR) data is challenging and expensive. The use of scale models and radar systems of such large targets saves time and reduces facility requirements. This study examines the feasibility of performing ATR on 1:144 scale model airplanes at Ka-band. The scale model and Ka-band radar simulate the collection of full-scale targets at VHF-band. The phase history measurement collections were completed in the Sensors and Signals Exploitation Laboratory (SSEL) at Wright State University. To ensure sufficient data for training and testing, the phase history data was augmented through mathematical translation and rotation of the scene. These augmented images were processed using the polar format algorithm and subsequently classified using support vector machines and convolutional neural networks. The resulting ATR models achieved a classification accuracy of over 82 percent for all aircraft types, except for the very similar B747-8 and B747-8F, which exhibited misclassification rates consistent with expectations for such similar targets.
Page Count
77
Department or Program
Department of Electrical Engineering
Year Degree Awarded
2024
Copyright
Copyright 2024, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
