Author : Christopher Z. Au
Publisher :
ISBN 13 :
Total Pages : 36 pages
Book Rating : 4.:/5 (119 download)
Book Synopsis Characterization of Deep Neural Network Feature Space for Inverse Synthetic Aperture Radar Automatic Target Recognition by : Christopher Z. Au
Download or read book Characterization of Deep Neural Network Feature Space for Inverse Synthetic Aperture Radar Automatic Target Recognition written by Christopher Z. Au and published by . This book was released on 2020 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.