Publication Date
2021
Document Type
Thesis
Committee Members
Joshua Ash, Ph.D. (Advisor); Tanvi Banerjee, Ph.D. (Committee Member); Mateen Rizki, Ph.D. (Committee Member)
Degree Name
Master of Science in Computer Engineering (MSCE)
Abstract
We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining procedure. In this work, we consider development of DNN methods that are robust to discrepancies between training and testing conditions. We examine several approaches to this problem, including using input-layer dropout, augmented data support indicators, and DNN-based robust approximate message passing.
Page Count
61
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2021
Copyright
Copyright 2021, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
