Cognitive Electronic Warfare In The Continuous Control Missions Of Unmanned Aerial Vehicles: A Comprehensive Review From An Artificial Intelligence Perspective

Abstract

This study examines cognitive architectures that enable Unmanned Aerial Vehicles (UAVs) to autonomously sustain continuous control tasks such as target search, detection, acquisition, and tracking under asymmetric Electronic Warfare (EW) threats. The inadequacy of traditional rule-based algorithms against dynamic jamming and spoofing attacks has necessitated the evolution of platforms into systems with high situational awareness and learning capabilities. Through AI-driven hybrid processes, aerial platforms gain the competence to react instantaneously to asymmetric and unpredictable conditions, thereby executing their missions end-to-end in an uninterrupted manner. In this context, the study investigates the relationship between the continuous control tasks of UAVs and Cognitive Electronic Warfare (CEW) in the modern battlefield, and presents model examples commonly used in the literature for each task. Furthermore, the role of adaptive control mechanisms in preserving flight stability against cyber-physical manipulations is analyzed. The models examined reveal that UAV systems with constrained hardware resources can achieve operational continuity and tactical superiority in intensive EW environments. The presented cognitive framework serves as a guide for transforming the UAV continuous task cycle into a hybrid ecosystem with spectrum awareness, integrated with EW elements.