Gurbuz Sevgi Zubeyde (ed.) Deep Neural Network Design for Radar Applications

SciTech Publishing, 2021. — 897 p. — ISBN: 978178561853.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.Fundamentals
Radar systems, signals, and phenomenology.
Basic principles of machine learning.
Theoretical foundations of deep learning.
Special topics
Radar data representation for classification of activities of daily living.
Challenges in training DNNs for classification of radar micro-Doppler signatures.
Machine learning techniques for SAR data augmentation.
Applications
Classifying micro-Doppler signatures using deep convolutional neural networks.
Deep neural network design for SAR/ISAR-based automatic target recognition.
Deep learning for passive synthetic aperture radar imaging.
Fusion of deep representations in multi static radar networks.
Application of deep learning to radar remote sensing.