Title Statements
- Full title :Deep neural network design for radar applications / edited by Sevgi Zubeyde Gurbuz
Edition Statements
- Publication Date:©2021
Publication Statements
- Publisher(s):SciTech Publishing, an imprint of The Institution of Engineering and Technology
- Place of Publication:London
- Language:eng
- Publication Date:©2021
The Physical Object
- Description:1 online resource : illustrations
Edition Identifiers
- ISBN:9781785618536, 1785618539
- oclc:on1228888932
- LC Classification:TK6575
Table of Contents
Prologue : perspectives on deep learning of RF data / Sevgi Zubeyde Gurbuz and Eric S. Mason -- Radar systems, signals, and phenomenology / Sevgi Zubeyde Gurbuz, Shunqiao Sun, and David Tahmoush -- Basic principles of machine learning / Ali Cafer Gurbuz and Fauzia Ahmad -- Theoretical foundations of deep learning / Stefan Brüggenwirth and Simon Wagner -- Radar data representation for classification of activities of daily living / Baris Erol and Moeness G. Amin -- Challenges in training DNNs for classification of radar micro-Doppler signatures / Sevgi Z. Gurbuz, Moeness G. Amin, Mehmet S. Seyfioğlu, and Baris Erol -- Machine learning techniques for SAR data augmentation / Benjamin Lewis, Theresa Scarnati, Michael Levy, John Nehrbass, Edmund Zelnio, and Elizabeth Sudkamp -- Classifying micro-Doppler signatures using deep convolutional neural networks / Youngwook Kim -- Deep neural network design for SAR/ISAR-based automatic target recognition / Simon Wagner and Stefan Brüggenwirth -- Deep learning for passive synthetic aperture radar imaging / Samia Kazemi, Eric Mason, Bariscan Yonel, and Birsen Yazici --Fusion of deep representations in multistatic radar networks / Jarez Satish Pat…
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