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Deep Neural Network Design for Radar Applications (Hardcover)
Loot Price: R3,076
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Deep Neural Network Design for Radar Applications (Hardcover)
Series: Radar, Sonar and Navigation
Expected to ship within 10 - 15 working days
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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.
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