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Deep Neural Network Design for Radar Applications

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Editor: Sevgi Zubeyde Gurbuz 1
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Publication Year: 2020

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.

Inspec keywords: sensor fusion; remote sensing by radar; radar target recognition; Doppler radar; radar computing; passive radar; classification; radar imaging; data structures; radar applications; learning (artificial intelligence); synthetic aperture radar; convolutional neural nets

Other keywords: radar remote sensing; deep convolutional neural networks; radar applications; radar signals; ISAR-based automatic target recognition; radar microDoppler signatures classification; SAR-based automatic target recognition; radar systems; SAR data augmentation; passive synthetic aperture radar imaging; deep representations fusion; machine learning; radar phenomenology; theoretical foundations; multistatic radar networks; daily living activities classification; deep learning; deep neural network design; DNN training; RF data; radar data representation

Subjects: Optical, image and video signal processing; Electrical engineering computing; Radar and radionavigation; General electrical engineering topics; Sensor fusion; Geophysical techniques and equipment; General and management topics; Knowledge engineering techniques; Signal processing and detection; Neural computing techniques

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