Название: Adaptive Radar Detection: Model-Based, Data-Driven and Hybrid Approaches
Автор: Angelo Coluccia
Издательство: Artech House
Год: 2022
Страниц: 235
Язык: английский
Формат: pdf (true)
Размер: 30.3 MB

This book shows you how to adopt data-driven techniques for the problem of radar detection, both per se and in combination with model-based approaches. In particular, the focus is on space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. It is a handy, concise reference for many classic (model-based) adaptive radar detection schemes as well as the most popular Machine Learning techniques (including deep neural networks) and helps you identify suitable data-driven approaches for radar detection and the main related issues. You’ll learn how data-driven tools relate to, and can be coupled or hybridized with, traditional adaptive detection statistics; understand fundamental concepts, schemes, and algorithms from statistical learning, classification, and neural networks domains. The book also walks you through how these concepts and schemes have been adapted for the problem of radar detection in the literature and provides you with a methodological guide for the design, illustrating different possible strategies. You’ll be equipped to develop a unified view, under which you can exploit the new possibilities of the data-driven approach even using simulated data.

Chapter 1 concisely presents the fundamentals of radar and signal detection, then reviews the most important design ideas behind classical adaptive radar detectors. Emphasis is on providing insights and drawing relationships between different model-based solutions, rather than on a detailed treatment. This is a distinctive angle compared to the many excellent sources already available in the literature, and ultimately provides a handy reference to popular detectors based on traditional model-based tools, highlighting their intrinsic interpretability and control onto the achievable performance.

Chapter 2 introduces the necessary theoretical elements and presents the main algorithmic tools adopted in Machine Learning, in particular for classification problems (of which detection is a special case). Classical hypothesis testing theory, including the Neyman-Pearson rationale, is reviewed and put in connection with the use of loss functions in classification problems, following the formulation of statistical learning. Essential principles, language, and concepts of the data-driven paradigm found in Machine Learning and data science are introduced and adapted to the typical background, jargon, and needs of engineers in the radar community. Several popular machine learning algorithms are compendiously described to provide a self-consistent reference.

Chapter 3 details the applications of the tools introduced in Chapter 2 to the radar field. The chapter specifically discusses how Machine Learning and more generally data-driven tools have been applied to the problem of target detection in the literature, but other applications of Machine Learning techniques, including signal classification, are also reviewed.

Chapter 4 is devoted to various forms of hybridization between model-based and data-driven concepts.

Chapter 5 provides an articulate discussion on some important open issues of data-driven techniques.

This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.