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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2014 / Cilt: 16 - Sayı: 48

DIELECTRIC SPHERICAL TARGET CLASSIFICATION USING TIME DOMAIN SCATTERING SIGNAL FEATURES AND ARTIFICIAL NEURAL NETWORKS

ZAMAN BÖLGESİ SAÇILIM SİNYALLERİNİN YAPISAL ÖZNİTELİKLERİ VE YAPAY SİNİR AĞLARI KULLANILARAK DİELEKTRİK KÜRESEL HEDEFLERİN SINIFLANMASI

Mehmet Mert TAYGUR
Alper SELVER
Yeşim ZORAL
Pages
104–108
DOI
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AI Summary & Key Takeaways

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

System performance is investigated by simulations based on dielectric spheres

📌 3 Key Takeaways

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Objective & Hypothesis:Solving the problem of classifying the targets, which look-alike, using electromagnetic scattering signals, is difficult as it heavily depends on the aspect angle.
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Key Findings & Data:For removing this dependency, distinctive features should be extracted from the signals and they should be processed.
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Conclusion & Impact:System performance is investigated by simulations based on dielectric spheres
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Radar · Öznitelik çıkarımı · Sınıflama

Abstract

Solving the problem of classifying the targets, which look-alike, using electromagnetic scattering signals, is difficult as it heavily depends on the aspect angle. For removing this dependency, distinctive features should be extracted from the signals and they should be processed. A target classification technique for objects having spherical shape, is proposed in this study. A set of features is extracted from the scattered signals first. Then a classifier, based on artificial neural networks, is designed with these features. System performance is investigated by simulations based on dielectric spheres

Özet

Benzer şekilli cisimlerden saçılan elektromanyetik dalgaları kullanarak ayırt etme probleminin çözümü, geliş açısına bağımlılıktan dolayı oldukça zordur. Bu bağımlılığı ortadan kaldırmak için, saçılım sinyallerinden ayırıcı öznitelikler çıkarılmalı ve işlenmelidir. Bu çalışmada, küresel geometriye sahip dielektrik hedeflerin tanınması için bir sınıflandırma yöntemi önerilmektedir. İlk olarak saçılım sinyallerinin yapısal özellikleri kullanılarak öznitelikler elde edilmiştir. Daha sonra bu öznitelikler ile yapay sinir ağı tabanlı bir sınıflayıcı tasarlanmıştır. Dielektrik küreler temel alınarak yapılan benzetimler ile sistem başarımı incelenmiştir

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Cite This Article

TAYGUR, M. M., SELVER, A., ZORAL, Y. (2014). DIELECTRIC SPHERICAL TARGET CLASSIFICATION USING TIME DOMAIN SCATTERING SIGNAL FEATURES AND ARTIFICIAL NEURAL NETWORKS. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 16 - Sayı: 48, 104–108. https://ajindex.com/articles/213388

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References (6)

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