ARTIFICIAL INTELLIGENCE-DRIVEN ELECTRONIC WARFARE SYSTEMS: THE FUTURE OF SPECTRUM DOMINANCE IN MODERN CONFLICT

Abstract

Electronic Warfare (EW) has re-emerged as a central domain of military competition, enabling forces to sense, exploit, and deny the electromagnetic spectrum (EMS). Recent advances in artificial intelligence (AI) and machine learning (ML), including deep learning, online learning, and reinforcement learning – promise to transform EW from largely manual, rule-based tactics into adaptive, automated systems capable of rapid detection, classification, deception, and autonomous resource allocation. This paper surveys the state of AI-driven EW, identifies key technical and doctrinal gaps, and proposes a framework for integrating AI agents across the EW mission stack (electronic support, protection, and attack). We analyze representative use cases - emitter identification, cognitive jamming, spectrum management, and autonomous counter-drone operations and evaluate the main challenges: adversarial robustness, data-scarcity and provenance, real-time constraints, interpretability, and legal/ethical governance. Finally, we propose a research agenda and architectural principles for deploying trustworthy, mission-centric AI-EW systems that balance autonomy with human oversight, and discuss implications for strategy, procurement, and coalition operations.