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Deep neural network design for radar applications
Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scienti...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London
SciTech Publishing, an imprint of The Institution of Engineering and Technology
2020
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a22000007c 4500 | ||
|---|---|---|---|
| 001 | KNOVEL_on1228888932 | ||
| 003 | OCoLC | ||
| 005 | 20231027140348.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 201221t20202021enka ob 000 0 eng d | ||
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| 019 | |a 1233057752 | ||
| 020 | |a 9781785618536 |q electronic bk. | ||
| 020 | |a 1785618539 |q electronic bk. | ||
| 020 | |z 9781785618529 | ||
| 020 | |z 1785618520 | ||
| 024 | 7 | |a 10.1049/SBRA529E |2 doi | |
| 035 | |a (OCoLC)1228888932 |z (OCoLC)1233057752 | ||
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| 072 | 7 | |a C7410 |2 inspec | |
| 082 | 0 | 4 | |a 621.3848 |q OCoLC |2 23/eng/20230216 |
| 049 | |a UAMI | ||
| 245 | 0 | 0 | |a Deep neural network design for radar applications |c edited by Sevgi Zubeyde Gurbuz |
| 264 | 1 | |a London |b SciTech Publishing, an imprint of The Institution of Engineering and Technology |c 2020 | |
| 264 | 4 | |c ©2021 | |
| 300 | |a 1 online resource |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | |a Description based on online resource; title from PDF title page (IET Digital Library , viewed on March 17, 2021) | ||
| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | |a 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 Patel, Francesco Fioranelli, Matthew Ritchie and Hugh Griffiths -- Application of deep learning to radar remote sensing / John Rogers, Lucas Cagle, John E. Ball, Mehmet Kurum and Sevgi Z. Gurbuz -- Epilogue : looking toward the future / Sevgi Zubeyde Gurbuz. | |
| 520 | |a Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction. Edited by an acknowledged expert, and with contributions from an international team of authors, this book provides a solid introduction to the fundamentals of radar and machine learning, and then goes on to explore a range of technologies, applications and challenges in this developing field. This book is also a valuable resource for both radar engineers seeking to learn more about deep learning, as well as computer scientists who are seeking to explore novel applications of machine learning. In an era where the applications of RF sensing are multiplying by the day, this book serves as an easily accessible primer on the nuances of deep learning for radar applications. | ||
| 590 | |a Knovel |b ACADEMIC - Aerospace & Radar Technology | ||
| 590 | |a Knovel |b ACADEMIC - Software Engineering | ||
| 650 | 0 | |a Radar. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Remote sensing. | |
| 650 | 0 | |a Doppler radar. | |
| 650 | 2 | |a Radar | |
| 650 | 2 | |a Neural Networks, Computer | |
| 650 | 2 | |a Remote Sensing Technology | |
| 650 | 6 | |a Radar. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Télédétection. | |
| 650 | 6 | |a Radar Doppler. | |
| 650 | 7 | |a radar. |2 aat | |
| 650 | 7 | |a Doppler radar |2 fast |0 (OCoLC)fst00896950 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 650 | 7 | |a Radar |2 fast |0 (OCoLC)fst01086712 | |
| 650 | 7 | |a Remote sensing |2 fast |0 (OCoLC)fst01094469 | |
| 650 | 7 | |a classification. |2 inspect | |
| 650 | 7 | |a convolutional neural nets. |2 inspect | |
| 650 | 7 | |a data structures. |2 inspect | |
| 650 | 7 | |a Doppler radar. |2 inspect | |
| 650 | 7 | |a learning (artificial intelligence). |2 inspect | |
| 650 | 7 | |a passive radar. |2 inspect | |
| 650 | 7 | |a radar applications. |2 inspect | |
| 650 | 7 | |a radar computing. |2 inspect | |
| 650 | 7 | |a radar imaging. |2 inspect | |
| 650 | 7 | |a radar target recognition. |2 inspect | |
| 650 | 7 | |a remote sensing by radar. |2 inspect | |
| 650 | 7 | |a sensor fusion. |2 inspect | |
| 650 | 7 | |a synthetic aperture radar. |2 inspect | |
| 653 | |a deep neural network design | ||
| 653 | |a radar applications | ||
| 653 | |a deep learning | ||
| 653 | |a RF data | ||
| 653 | |a radar systems | ||
| 653 | |a radar signals | ||
| 653 | |a radar phenomenology | ||
| 653 | |a machine learning | ||
| 653 | |a theoretical foundations | ||
| 653 | |a radar data representation | ||
| 653 | |a daily living activities classification | ||
| 653 | |a DNN training | ||
| 653 | |a radar microDoppler signatures classification | ||
| 653 | |a SAR data augmentation | ||
| 653 | |a deep convolutional neural networks | ||
| 653 | |a SAR-based automatic target recognition | ||
| 653 | |a ISAR-based automatic target recognition | ||
| 653 | |a passive synthetic aperture radar imaging | ||
| 653 | |a deep representations fusion | ||
| 653 | |a multistatic radar networks | ||
| 653 | |a radar remote sensing | ||
| 700 | 1 | |a Gurbuz, Sevgi Zubeyde |e editor | |
| 856 | 4 | 0 | |u https://appknovel.uam.elogim.com/kn/resources/kpDNNDRA0A/toc |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37507071 | ||
| 994 | |a 92 |b IZTAP | ||