دانلود کتاب جنگ الکترونیک شناختی

Cognitive electronic warfare (CogEW) has undergone a remarkable transformation in the past four years, propelled by advancements in artificial intelligence (AI), machine learning (ML), and software-defined radios. Once reliant on static, rulebased systems, modern EW now embraces adaptive, autonomous technologies capable of sensing, learning, and dynamically responding to complex electromagnetic environments. Breakthroughs in deep reinforcement learning now empower systems to optimize jamming and interception strategies in rapid real time. Spectrum situation awareness and signal classification enhance situation awareness, while edge computing accelerates decentralized decision making. Techniques such as transfer learning enable rapid adaptation to new signal types with limited data, while emerging methods like zero-shot learning show potential for identifying previously unseen signals based on high-level signal features. At the same time, adversarial ML introduces both new vulnerabilities and innovative methods to counter AI-based threats. This edition captures the changes in technology that have taken place since the last edition in 2021. Most of these changes are in AI, which is exploding in a tidal wave of change; a few developments are in EW or hardware. The book went from 246 pages to 407, with lots of new material and restructuring of previous material. The major changes are: • In the first edition, we expected that most of our readers would be radio frequency (RF) experts, and that we didn’t need to explain the concepts of the electromagnetic spectrum (EMS) or how radio or radars work. We learned from AI experts that this was the first time someone had explained the RF problem in words that they could understand. For that reason, we added more discussion of the EMS, including a conversation of civilian EW. The mapping between cognitive radio, cognitive radar, and cognitive EW is now more highlighted. • We added conversation about some of the modern AI tools, including spiking neural networks, large language models, generative AI, and deep reinforcement learning. We also added a discussion about how to build models that work on the edge, with limited compute, power, and communications. There is now more explicit conversation about reward hacking. • We significantly rewrote the “Test & Evaluation” chapter to better capture the challenges of testing systems that learn after they have been deployed. Testing AI-enabled systems is a popular topic currently; we take the extra step of discussing the difference between AI-enabled and cognitive, and what that means for assuring cognitive systems. • We added a variety of projects with the goal of making practical, bite-size examples to get you started. Each idea could grow into larger, deployable systems. In the same vein, we added more examples of how the cognitive concepts would appear in an RF system, including systems development and a longer conversation about EW battle damage assessment (EW BDA). • We added a discussion on some of the newer hardware, including neuromorphics, photonics, and quantum. Metasurfaces—the technology behind invisibility cloaks—are already a reality, playing a crucial role in 6G. In EW, they are unlocking innovations in sensing, stealth, communications, and radar. Metamaterials are perhaps the strongest motivating factor for fast real-time optimization and learning in EW. • We expanded our discussion of data management, including semantics, metadata, and schemas. We also grew the list of RF datasets; while the number of public RF datasets is still small, it is no longer embarrassingly short. • We added a full section on the speed of AI, trying to describe the rate of change. Perhaps the strongest indicator of the speed of change is that there were daily updates. DeepSeek, the model that shook the world in mid-Jan 2025, was overtaken by two other models in less than two weeks. It felt like quicksand: By the time we had completed a sentence, it would already be outdated. Almost 40% of the citations in this edition are to works published in 2021 or later, and we barely scratched the surface of what’s available. The lesson learned for readers is that any citation or recommendation that a technique works well should be taken with a grain of salt: There’s a very good chance that something better has come out. We hope that we’ve given you enough of the ideas and keywords to be able to think creatively and search effectively.