Drone Swarm Classification from ISAR Imaging

Remziye Büşra Çoruk; Ali Kara; Elif Aydın

doi:10.53525/jster.1529575

TR EN

ISAR Görüntülemesi kullanılarak Drone Sürüsü Sınıflandırması

Öz

Günümüz teknolojisinde internet üzerinden kolaylıkla satın alınabilmesi ve kolaylıkla geliştirilebilmesi nedeniyle drone kullanımı oldukça popüler hale gelmiştir. Geniş kullanım alanına sahip drone'lar ile sürü yapıları popüler hale geldi. Ancak bu durum bazı sorunları da beraberinde getirdi. Hava sahasındaki drone sürülerinin kontrolsüz kullanımının önüne geçmek amacıyla drone tespiti konusu ortaya çıktı. Drone sürüsü tespiti, tehlikeli kazaları veya suç teşkil eden eylemleri önlemek için önemlidir. Bu çalışmada, drone sürülerinin çeşitli oluşum sürü türlerine dayalı ters sentetik açıklıklı radar (ISAR) görüntüleri kullanılarak derin öğrenme ile yeni bir sınıflandırma algoritması önerilmektedir. ISAR görüntüleri ANSYS simülasyonu kullanılarak oluşturulur. Ek olarak, yüksek hızlı hesaplama için yüksek frekanslı yapısal simülatör (HFSS) - sıçrayan ışın (SBR+) çözücüsü kullanılır. ISAR görüntülerinin elde edilmesine yönelik radar ve simülasyon parametreleri tartışılmıştır. Yüksek çözünürlük elde etmek için özellikle alt aralık ve çapraz aralık çözünürlük parametreleri dikkate alınır. ISAR görüntüleri oluşum açısından derin öğrenme yöntemleri kullanılarak sınıflandırılır. Formasyon türleri arasında Çizgi, Kare, Çapraz ve Üçgen bulunur. Evrişimli sinir ağı (CNN) modeli, sınıflandırma problemlerini çözmek için kullanılır. Model; eğitim, doğrulama ve test adımlarından oluşur. Sınıflandırma performansı sonuçları yüksek doğrulukla sunulur. Geliştirilen yöntem drone karşıtı teknolojiler için kullanılabilir

Anahtar Kelimeler

Radar sistemleri , Ters sentetik açıklıklı radar görüntüleme , sınıflandırma , konvolüsyonel sinir ağları

Drone Swarm Classification from ISAR Imaging

Abstract

In today's technology, the use of drones has become very popular for they can be easily purchased over the Internet and can be easily developed. With drones that have wide usage areas, swarm structures have become popular. However, this has brought about some problems. The issue of drone detection has emerged in order to prevent the uncontrolled use of drone swarms in the airspace. Drone swarm detection is important to prevent dangerous accidents or criminal acts. In this study, a new classification algorithm is proposed with deep learning using inverse synthetic aperture radar (ISAR) images of drone swarms based on various formation swarm types. ISAR images are created using ANSYS simulation. Additionally, high frequency structural simulator (HFSS) - shooting bouncing ray (SBR+) solver is used for high-speed computation. Radar and simulation parameters to obtain ISAR images are discussed. Especially, down-range and cross-range resolution parameters are taken into account to achieve high resolution. ISAR images are classified using deep learning methods in terms of formation. Formation types include Line, Square, Cross, and Triangle. The convolutional neural network (CNN) model is used to solve classification problems. The model consists of train, validation, and test steps. Classification performance results are presented with high accuracy. The developed method can be used for anti-drone technologies.

Keywords

Radar Systems , Inverse synthetic aperture radar imaging , Drone , Classification , Convolution neural network

References

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Details

Primary Language

English

Subjects

Wireless Communication Systems and Technologies (Incl. Microwave and Millimetrewave)

Journal Section

Research Article

Authors

Remziye Büşra Çoruk ^*
0000-0002-9466-3862
Türkiye

Ali Kara
0000-0002-9739-7619
Türkiye

Elif Aydın
0000-0001-6878-1796
Türkiye

Publication Date

December 21, 2024

Submission Date

August 7, 2024

Acceptance Date

September 23, 2024

Published in Issue

Year 2024 Volume: 5 Number: 2

DOI

https://doi.org/10.53525/jster.1529575

IZ

https://izlik.org/JA84NU48DL

APA

Çoruk, R. B., Kara, A., & Aydın, E. (2024). Drone Swarm Classification from ISAR Imaging. Journal of Science, Technology and Engineering Research, 5(2), 127-134. https://doi.org/10.53525/jster.1529575

AMA

1.Çoruk RB, Kara A, Aydın E. Drone Swarm Classification from ISAR Imaging. Journal of Science, Technology and Engineering Research. 2024;5(2):127-134. doi:10.53525/jster.1529575

Chicago

Çoruk, Remziye Büşra, Ali Kara, and Elif Aydın. 2024. “Drone Swarm Classification from ISAR Imaging”. Journal of Science, Technology and Engineering Research 5 (2): 127-34. https://doi.org/10.53525/jster.1529575.

EndNote

Çoruk RB, Kara A, Aydın E (December 1, 2024) Drone Swarm Classification from ISAR Imaging. Journal of Science, Technology and Engineering Research 5 2 127–134.

IEEE

[1]R. B. Çoruk, A. Kara, and E. Aydın, “Drone Swarm Classification from ISAR Imaging”, Journal of Science, Technology and Engineering Research, vol. 5, no. 2, pp. 127–134, Dec. 2024, doi: 10.53525/jster.1529575.

ISNAD

Çoruk, Remziye Büşra - Kara, Ali - Aydın, Elif. “Drone Swarm Classification from ISAR Imaging”. Journal of Science, Technology and Engineering Research 5/2 (December 1, 2024): 127-134. https://doi.org/10.53525/jster.1529575.

JAMA

1.Çoruk RB, Kara A, Aydın E. Drone Swarm Classification from ISAR Imaging. Journal of Science, Technology and Engineering Research. 2024;5:127–134.

MLA

Çoruk, Remziye Büşra, et al. “Drone Swarm Classification from ISAR Imaging”. Journal of Science, Technology and Engineering Research, vol. 5, no. 2, Dec. 2024, pp. 127-34, doi:10.53525/jster.1529575.

Vancouver

1.Remziye Büşra Çoruk, Ali Kara, Elif Aydın. Drone Swarm Classification from ISAR Imaging. Journal of Science, Technology and Engineering Research. 2024 Dec. 1;5(2):127-34. doi:10.53525/jster.1529575

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