This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.

This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.

Electronic warfare is a critical component of modern military operations and has undergone significant advances in recent years. This book provides an overview of electronic warfare, its historical development, key components, and its role in contemporary conflict scenarios. It also discusses emerging trends and challenges in electronic warfare and its contemporary relevance in an era of advanced technology and cyber threats, emphasizing the need for continued research and development in this area. The book explores the burgeoning intersection of artificial intelligence and electronic warfare, highlighting the evolving landscape of modern conflicts and the implications of integrating advanced technologies. The multifaceted roles of artificial intelligence in electronic warfare are highlighted, examining its potential advantages, ethical considerations, and challenges associated with its integration. CONTENTS: Abstract Abbreviations Introduction - Electronic warfare - - Definitions - - Historical development - - The key components - - - Electronic attack (EA) - - - Electronic protection - - - Electronic support - Techniques and tactics - EW systems - - Radar - Relationship of EW to other combat capabilities - - Cyber electronic warfare - The main competitors - - US - - China - - Russia - - NATO - - European Union - Challenges and trends - Asymmetric warfare Artificial intelligence - The historical background of electronic warfare - The role of artificial intelligence in electronic warfare - - Specific applications - AI techniques - - Machine learning - - Fuzzy systems - - Genetic algorithm - Trends - Challenges and risks - - Ethical considerations - Cognitive EW Conclusion Bibliography DOI: 10.58679/MM14430

This popular series of tutorials, featured over a period of years in the Journal of Electronic Defense, is now available in a single volume. Organized into chapters with new introductory and supplementary material from the author, you get clear, concise and well-illustrated examinations of critical topics such as antenna parameters, receiver sensitivity, processing tasks, and search strategies, LPI signals, jamming, communication links, and simulation. The chapters define key terms and explain how and why particular technologies are relevant to electronic defense. Detailed charts, diagrams and formulas give you the practical knowledge you need to apply specific techniques in the field.

Here's an advanced practitioner's guide to the latest concepts and threats associated with modern electronic warfare (EW). This new book identifies and explains the newest radar and communications threats, and provides EW and radar engineers, managers, and technical professionals with practical, "how-to" information on designing and implementing ECM and ECCM systems.

This book presents a unique and comprehensive introduction to performance prediction of directed energy (DE) systems using mathematical modeling frameworks, with focus on high power radio frequency and high energy laser performance. It provides system designers with a means for predicting DE system performance and measuring the required power levels necessary to neutralize targets including UAVs and other unmanned swarms. The book begins with a systematic and concise overview of DE systems, including its historical roots. You will then learn how to develop effective mathematical models and understand how to use these models to implement safer and efficient use of DE systems in various scenarios. A special section is devoted to examples and attributes of unmanned systems since these are viewed as primary targets suitably disabled by DE systems. There is also an extensive survey of the relevant tools of mathematics and physics for DE systems, together with a series of pertinent references you can follow for further information. The final section of the book outlines potential future research directions that interested researchers are encouraged to pursue. With its exclusive and unmatched coverage of predictive modeling for DE systems and performance, this is an important book for engineers and operators working in the defense industry, including government and private contractors, as well as research practitioners at universities and engineering organizations worldwide working in the field of DE system performance.

In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.

This book provides a comprehensive resource and thorough treatment in the latest development of Digital RF Memory (DRFM) technology and their key role in maintaining dominance over the electromagnetic spectrum. Part I discusses the use of advanced technology to design transceivers for spectrum sensing using unmanned systems to dominate the electromagnetic spectrum. Part II uses artificial intelligence and machine learning to enable modern spectrum sensing and detection signal processing for electronic support and electronic attack. Another key contribution is examination of counter-DRFM techniques. DRFM and transceiver design details and examples are provided along with the MATLAB software allowing the reader to construct their own embedded DRFM transceivers for unmanned systems. It examines the design trade-offs in developing multiple, structured, false target synthesis DRFM architectures and aids in developing counter-DRFM techniques and distinguish false target from real ones. Written by an expert in the field, and including MATLAB™ design software, this is the only comprehensive book written on the subject of DRFM.

Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]