About this book:

*AI-Powered Electronic Warfare* explores the transformative integration of machine learning and automation into the electromagnetic battlespace. The book begins by establishing the technical foundations of modern signals intelligence, covering digital signal processing, feature engineering, and the rigorous data curation required to train RF-specific models. It details how supervised learning enables the rapid classification of complex modulations and specific emitters, while unsupervised techniques allow for the detection of anomalies, emergent threats, and subtle changes within increasingly dense and congested spectral environments.

The text advances into the realm of cognitive electronic warfare, where reinforcement learning and generative models create adaptive decision loops for real-time jamming and spectrum maneuver. By moving beyond static, library-based responses, these intelligent systems can observe adversary behavior, orient to novel waveforms, and autonomously deploy surgical electronic attacks or deceptive countermeasures. Special emphasis is placed on Navigation Warfare (NAVWAR), highlighting the vulnerabilities of GNSS and the AI-driven sensor fusion techniques necessary to maintain resilient positioning, navigation, and timing in contested zones.

Practical implementation is addressed through chapters on MLOps, real-time processing architectures, and edge computing, which are essential for deploying low-latency AI on tactical platforms. The book also provides a critical framework for testing, evaluation, and verification, stressing the importance of adversarial machine learning defenses to protect against signal-based "mind games." These technical discussions are balanced with operational case studies from recent conflicts, illustrating the real-world shift toward machine-speed engagements and the shrinking of the OODA loop.

The final section examines the human and strategic dimensions of AI-enabled conflict, prioritizing human-machine teaming and the cultivation of operator trust through explainable AI. The book concludes by addressing the legal, ethical, and policy implications of autonomous electronic warfare, such as accountability and adherence to international humanitarian law. Looking toward the future, it predicts an era of multi-domain convergence where AI-driven electronic warfare, cyber operations, and kinetic strikes become indistinguishable, fundamentally redefining global strategic stability.

What You'll Find Inside:

• AI-driven cognitive electronic warfare systems that close the OODA loop for real-time adaptive spectrum maneuver and jamming at machine speed
• Machine learning techniques for RF signal detection, classification, and geolocation in dense, congested electromagnetic spectra
• Adaptive electronic attack methods including reinforcement learning for jamming control and generative models for signal synthesis and deception
• Robustness frameworks for defending against adversarial machine learning and ensuring reliable performance across lab, range, and operational environments
• Human-machine teaming principles and ethical considerations for building trustworthy AI-enabled EW systems that amplify rather than replace human judgment

Who's It For:

This book is essential for electronic warfare engineers, signal intelligence analysts, defense researchers, and military decision-makers who need to implement or evaluate AI-enhanced EW systems. It bridges technical machine learning concepts with operational realities, providing practical frameworks for spectrum dominance in modern conflict while addressing critical considerations like robustness, ethics, and human-autonomy teaming.