Characterization of deep neural network feature space for inverse synthetic aperture radar automatic target recognition
Name
1192538763-MIT.pdf
Size
909.09 KB
Format
Adobe PDF
Checksum (MD5)
af6c1852f4df7873b043a2f0dc2894c0
Author(s)
Au, Christopher Z.
Advisor(s)
Jing Kong and David Barrett.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
The Airborne Radar Systems and Techniques group at MIT Lincoln Laboratory trained neural networks to classify different targets at sea based on inverse synthetic aperture radar (ISAR) data. Simulated data was used to train these neural network based automatic target recognition (ATR) systems. The technical challenge of this project was to find a way to evaluate the quality and adequacy of a limited set of training data. Using simulated ISAR images to train neural networks, the project determined the minimum amount of variation in terms of parameters such as aspect angle to adequately train a neural network. Establishing a correspondence between training data variation and the resulting feature space of the data informed the minimum spanning-set of training data required for future data collects.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 35-37).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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