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Journals / Savunma Bilimleri Dergisi / 2026 / Cilt: 22 Sayı: 1

Deep Learning for Specific Emitter Identification in Modern Electronic Warfare: From Static Models to Cognitive Adaptation

Modern Elektronik Harpte Özgün Yayıcı Tanımlama İçin Derin Öğrenme: Statik Modellerden Bilişsel Adaptasyona

Mert Karahan(MİLLİ SAVUNMA ÜNİVERSİTESİ, KARA HARP OKULU, ELEKTRONİK VE HABERLEŞME MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-2884-9623
Onur Battal(MİLLİ SAVUNMA ÜNİVERSİTESİ, KARA HARP OKULU, ELEKTRONİK VE HABERLEŞME MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-4581-2857
Pages
293–306
Publication Date
2026-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Consequently, it is revealed that a transition from static models to self-healing and adaptive cognitive architecture is imperative for the sustainability of EW superiority.

📌 3 Key Takeaways

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Objective & Hypothesis:The complexity of the electromagnetic spectrum in the modern battlefield has made it critical for Cognitive Electronic Warfare (EW) systems to identify hostile elements not only by type but also at the individual identity level using Radio Frequency Fingerprints (RFF) originating from hardware imperfections, a process known as Specific Emitter Identification (SEI).
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Key Findings & Data:The analyses demonstrate that although static deep architectures such as Deep Residual Networks (ResNet) and Temporal Convolutional Networks (TCN) exhibit accuracies exceeding 96% in controlled laboratory environments, their reliability drops to levels around 50% under real-world field conditions such as data scarcity (few-shot), the presence of unknown threats (open-set), and adversarial attacks.
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Conclusion & Impact:Consequently, it is revealed that a transition from static models to self-healing and adaptive cognitive architecture is imperative for the sustainability of EW superiority.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Özgün Yayıcı Tanımlama (SEI) · Bilişsel Elektronik Harp · Derin Öğrenme

Abstract

The complexity of the electromagnetic spectrum in the modern battlefield has made it critical for Cognitive Electronic Warfare (EW) systems to identify hostile elements not only by type but also at the individual identity level using Radio Frequency Fingerprints (RFF) originating from hardware imperfections, a process known as Specific Emitter Identification (SEI). This study comprehensively examines the evolution of SEI literature from manual feature extraction to data-driven Deep Learning (DL) approaches from the perspectives of data preprocessing, architectural design, and dynamic environmental adaptation. The analyses demonstrate that although static deep architectures such as Deep Residual Networks (ResNet) and Temporal Convolutional Networks (TCN) exhibit accuracies exceeding 96% in controlled laboratory environments, their reliability drops to levels around 50% under real-world field conditions such as data scarcity (few-shot), the presence of unknown threats (open-set), and adversarial attacks. In this context, the study emphasizes the importance of multimodal feature fusion techniques, such as Variational Mode Decomposition (VMD) and Bispectrum analysis, to bridge the reliability gap; furthermore, it proposes the integration of Extreme Value Theory (EVT)-based open-set recognition and meta-learning strategies for the system to identify unknown threats and update itself autonomously. Consequently, it is revealed that a transition from static models to self-healing and adaptive cognitive architecture is imperative for the sustainability of EW superiority.

Özet

Modern savaş alanının elektromanyetik spektrumundaki karmaşıklık, Bilişsel Elektronik Harp (EH) sistemleri için düşman unsurlarının sadece tip bazında değil, donanım kusurlarından kaynaklanan Radyo Frekansı Parmak İzleri (RFF) kullanılarak bireysel kimlik bazında tanınmasını (Özgün Yayıcı Tanımlama - SEI) kritik hale getirmiştir. Bu çalışma, SEI literatürünün manuel öznitelik çıkarımından veri güdümlü Derin Öğrenme (DL) yaklaşımlarına olan evrimini; veri ön işleme, mimari tasarımı ve dinamik ortam adaptasyonu perspektifinden kapsamlı bir şekilde incelemektedir. Yapılan analizler, ResNet ve TCN gibi statik derin mimarilerin kontrollü laboratuvar ortamlarında %96'yı aşan başarımlar sergilemesine rağmen; veri kıtlığı (few-shot), bilinmeyen tehditlerin varlığı (open-set) ve karşıt saldırılar (adversarial attacks) gibi gerçek saha koşullarında güvenilirliklerinin %50 seviyelerine kadar düştüğünü göstermektedir. Bu bağlamda çalışma, güvenilirlik açığını kapatmak için Varyasyonel Mod Ayrıştırma (VMD) ve Bispektrum analizi gibi çok modlu (multimodal) özellik füzyonu tekniklerinin önemini vurgulamakta; ayrıca sistemin bilinmeyen tehditleri tanıması ve kendini otonom olarak güncellemesi için Ekstrem Değer Teorisi (EVT) tabanlı açık küme tanıma ve meta-öğrenme stratejilerinin entegrasyonunu önermektedir. Sonuç olarak, elektronik harp üstünlüğünün sürdürülebilirliği için statik modellerden, kendini onaran ve adapte olan bilişsel mimarilere geçişin zorunlu olduğu ortaya konulmuştur.

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Cite This Article

Karahan, M., Battal, O. (2026). Deep Learning for Specific Emitter Identification in Modern Electronic Warfare: From Static Models to Cognitive Adaptation. Savunma Bilimleri Dergisi, Cilt: 22 Sayı: 1, 293–306. https://doi.org/10.17134/khosbd.1898855

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