![]() The authors will briefly cover some popular optimization strategies that have been successfully applied and are quite relevant for radar applications that are sometimes quite different from the optical domain (e.g., scarce training data sets). The authors will introduce the backpropagation algorithm as a state-of-the-art method for training of neural networks. Then, the authors will address the mathematical model of the perceptron that still forms the basis of multilayer architectures. This chapter will hence begin with a short review of historical and biological introduction to the topic. Even though the concept and theory has been around since many decades, efficient deep learning methods were developed in the last years and made the approach computationally tractable. In contrast to shallow neural topologies, deep neural networks comprise more than one hidden layer of neurons. In this chapter, the authors will derive the theoretical foundations of deep neural network architectures. More recent learning approaches, such as variants of deep neural networks (DNNs), and more specific ML tools related to the various radar applications will follow in subsequent chapters of this book. In Section 2.4, we define various performance assessment metrics and describe the design and evaluation of a learning algorithm. We present several fundamental techniques of supervised and unsupervised learning in Section 2.3. In Section 2.2, we briefly describe the various components of an ML algorithm. We also present different tasks that ML can tackle under each category and provide relevant radar-based examples. In Section 2.1, we describe the concept of learning from data and introduce the main categories of ML, namely, supervised and unsupervised learning. We expect the reader to have background knowledge of basic linear algebra and probability theory, which form the foundations of ML. This chapter provides an overview of the basic principles of ML, outlining the fundamental concepts that need to be applied correctly for a broad range of radar applications. Optical, image and video signal processing Passive synthetic aperture radar imaging Radar microDoppler signatures classification 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. 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. 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. 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. 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. 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. 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. 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.
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