İnsansız Hava Araçlarının Sürekli Kontrol Görevlerinde Bilişsel Elektronik Harp: Yapay Zeka Perspektifinden Kapsamlı Bir İnceleme

Abstract

Bu çalışma, İnsansız Hava Araçlarının (İHA) asimetrik Elektronik Harp (EH) tehditleri altında hedef arama, tespit, kilitleme ve izleme gibi sürekli kontrol görevlerini özerk bir şekilde sürdürmesini sağlayan bilişsel mimarileri incelemektedir. Dinamik karıştırma ve sahte sinyal saldırılarına karşı geleneksel kural tabanlı algoritmaların yetersizliği, platformların yüksek durum farkındalığı ve öğrenme yeteneklerine sahip sistemlere dönüşmesini zorunlu kılmıştır. YZ odaklı hibrit süreçler sayesinde, hava platformları asimetrik ve öngörülemez koşullara anında tepki verme yeteneği kazanır ve böylece görevlerini kesintisiz bir şekilde baştan sona yerine getirir. Bu bağlamda çalışma, modern savaş alanında İHA'ların sürekli kontrol görevleri ile Bilişsel Elektronik Harp (BEH) arasındaki ilişkiyi ortaya koymaktadır ve her görev için literatürde yaygın olarak kullanılan model örneklerini sunar. Ayrıca, siber-fiziksel manipülasyonlara karşı uçuş stabilitesini korumada uyarlanabilir kontrol mekanizmalarının rolü analiz edilmektedir. İncelenen modeller, sınırlı donanım kaynaklarına sahip İHA sistemlerinin yoğun EH ortamlarında operasyonel süreklilik ve taktik üstünlük sağlayabileceğini ortaya koymaktadır. Sunulan bilişsel çerçeve, İHA'ların sürekli görev döngüsünü, EH unsurlarıyla entegre edilmiş, spektrum farkındalığına sahip bir hibrit ekosisteme dönüştürmek için bir kılavuz görevi görmektedir.

Keywords

• İnsansız Hava Araçları (İHA)
• Bilişsel Elektronik Harp (BEH)
• Sürekli Kontrol Görevleri
• Sensör Füzyonu
• Yapay Zeka (YZ)
• Makine Öğrenimi (MÖ)

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.

Keywords

• Unmanned Aerial Vehicles (UAVs)
• Cognitive Electronic Warfare (CEW)
• Continuous Control Tasks
• Sensor Fusion
• Artificial Intelligence (AI)
• Machine learning (ML)

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