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Radar signals are one of the most challenging signals to process, because of the extreme signal to noise ratio and the dynamic range of the signals. This book gives readers an analysis of the various tools available to help better understand radar data, including coverage of new machine learning and statistical methods.
Principles of Modern Radar: Basic Principles is a comprehensive text for courses in radar systems and technology, a professional training textbook for formal in-house courses and for new hires; a reference for ongoing study following a radar short course and a self-study and professional reference book.
Methods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.
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.
Collects the revised and updated versions of lectures presented at an advanced course on [title] held at the Accademia dei Lincei, Rome, 1988, as well as some additional chapters. The 13 chapters address basic concepts on detection, estimation, and optimum filtering; models of clutter; CFAR techniques in clutter; pulse compression and equivalent technologies; pulse doppler radar; MTI, MTD, and adaptive clutter cancellation; rejection of active interference; architecture and implementation of radar signal processors; identification of radar targets; phased arrays; bistatic radars; space-based radar; and evolution and future trends of radar. Primarily for radar engineers and researchers, as well as advanced students. Distributed by INSPEC. Annotation copyright by Book News, Inc., Portland, OR
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. This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.
This book text provides an overview of the radar target recognition process and covers the key techniques being developed for operational systems. It is based on the fundamental scientific principles of high resolution radar, and explains how the underlying techniques can be used in real systems, taking into account the characteristics of practical radar system designs and component limitations. It also addresses operational aspects, such as how high resolution modes would fit in with other functions such as detection and tracking.
This book serves as a handbook for radar active jamming system designers, in which design principles and methods are introduced in detail. The book starts from the basic concept and then discusses requirements analysis, type selection, key indicators description, and design methods of radar active jamming system and each subsystem step by step. The content is expressed in an intelligible way, and hence, it is easy to follow even for beginners in this area. Since the authors of this book are all experts and have designed plenty of real systems, their book certainly helps new engineers deal with different kinds of problems encountered while designing a radar active jamming system.
This book systematically describes advanced metric wave radar and its practical applications, offering a comprehensive introduction to the engineering design methods from the perspectives of system design, antenna/feed and transmit/receive subsystems, as well as mechanical structure design. Focusing on the height-finding method, it describes in detail how the super-resolution technique can be used to solve the problem of low-angle height finding in metric wave radar. It also discusses the anti-jamming method for the unique jamming environment. Further, it presents narrowband target recognition methods to overcome the limitations of narrow absolute bandwidth in metric wave radar and to further explore the technique’s potential. Cooperative detection for metric wave radar netting is also addressed, and the main experimental results are included. The book offers a valuable resource for professional engineers, researchers and teachers, as well as graduate students engaged in radar system engineering, electronic engineering, and signal processing.