دانلود کتاب اصول رادار مدرن، جلد ۲

Radar technology has continued to evolve rapidly, driven by several important trends. The root trend is the continued rapid advance of radio frequency (RF) and digital technology. This in turn has enabled the continued migration of the point of digitization toward the radar antenna, in turn allowing ever more of the radar functionality to be performed digitally rather than using analog techniques. Such “computed radar” systems can employ newer, more advanced techniques and algorithms to create systems with more advanced signal processing algorithms and, importantly, more complex and adaptive configuration and control processes. Ten years after publication of the first edition, it became evident to the publisher, IET, and the editors that a new edition of all three volumes was needed to keep pace with radar development and maintain the claim of representing modern radar. Planning for the second edition began in early 2020, and work commenced on the first volume, POMR: Basic Principles, that same year. The second edition of Basic Principles was completed and published in 2022 and is continuing the success of the first edition. This second edition of the second volume, Principles of Modern Radar: Advanced Techniques (POMR:AT), follows the 2013 original by twelve years. Of the three volumes, this one, as emphasized by its title, has the sharpest focus on advanced algorithms and processing techniques. As such, it is perhaps most affected by the rapid digital evolution of radar described above, and so has changed the most from the first to the second edition. The most obvious impact of increasing processing capability on POMR:AT is the addition of entire new chapters on spectrum sharing, machine learning in radar, and cognitive radar. The number of wireless communication services and radar systems competing for spectrum allocations has grown rapidly over the last decade, with no end in sight. The need for their coexistence has made requirements and techniques considered in Chapter 5 on spectrum sharing an essential design consideration in modern radar. As with many other technology areas, the recent emergence and meteoric rise of artificial intelligence (AI) and machine learning (ML) as a new paradigm for extracting information from data in complex sensing and processing scenarios offers the radar community new ways of addressing long-standing problems. This edition adds chapters addressing the application of AI and ML to radar at two different levels. Chapter 17 provides a basic tutorial on ML techniques and discusses usage examples such as enhanced detection in radio frequency interference (RFI) and clutter environments, improved target recognition and radar image enhancement, and others. Chapter 18 on cognitive radar addresses higher-level system configuration and control to improve system strategic decision-making. Advanced radars are highly dynamic systems able to probe their environment and adjust their frequencies, waveforms, antenna patterns, and more to optimize the collected data and maximize performance. This chapter describes how techniques from robotics and machine learning are being adopted to realize the potential of these systems.