Araştırma Makalesi
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Drone Swarm Classification from ISAR Imaging

Yıl 2024, Cilt: 5 Sayı: 2, 127 - 134
https://doi.org/10.53525/jster.1529575

Öz

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.

Kaynakça

  • [1] Coluccia, A., Parisi, G., & Fascista, A. (2020). Detection and classification of multirotor drones in radar sensor networks: A review. Sensors, 20(15), 4172. doi: 10.3390/s20154172
  • [2] Tang, J., Duan, H., & Lao, S. (2023). Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: A comprehensive review. Artificial Intelligence Review, 56(5), 4295-4327. doi: 10.1007/s10462-022-10281-7
  • [3] Ciattaglia, G., Temperini, G., Spinsante, S., & Gambi, E. (2021, June). mmWave Radar Features Extraction of Drones for Machine Learning Classification. In 2021 IEEE 8th International Workshop on Metrology for AeroSpace (MetroAeroSpace) (pp. 259-264). IEEE. doi: 10.1109/MetroAeroSpace51421.2021.9511703
  • [4] Chen, V. C. (2019). The micro-Doppler effect in radar. Artech house.
  • [5] Franceschetti, G., & Lanari, R. (2018). Synthetic aperture radar processing. CRC press.
  • [6] Gökdoğan, B. Y., Çoruk, R. B., Aydın, E., & Kara, A. 2D Millimeter-Wave SAR Imaging with Automotive Radar. Journal of Science, Technology and Engineering Research, 5(1), 68-77.
  • [7] Borkar, V. G., Ghosh, A., Singh, R. K., & Chourasia, N. (2010). Radar cross-section measurement techniques. Defence Science Journal, 60(2), 204-212. doi: 10.14429/dsj.60.341
  • [8] Yang, Y., Wang, X. S., Li, Y. Z., & Shi, L. F. (2019, September). RCS measurements and ISAR images of fixed-wing UAV for fully polarimetric radar. In 2019 International Radar Conference (RADAR) (pp. 1-5). IEEE. doi: 10.1109/RADAR41533.2019.171361
  • [9] Li, C. J., & Ling, H. (2016). An investigation on the radar signatures of small consumer drones. IEEE Antennas and Wireless Propagation Letters, 16, 649-652.
  • [10] Yang, Y., Wang, X. S., Li, Y. Z., & Shi, L. F. (2019, September). RCS measurements and ISAR images of fixed-wing UAV for fully polarimetric radar. In 2019 International Radar Conference (RADAR) (pp. 1-5). IEEE.
  • [11] Hamad, A., & Berens, P. (2024, July). 3D ISAR Imaging of an in-Air Rotating Drone Using Sparse Recovery and Multi-Channel Interferometry. In 2024 International Radar Symposium (IRS) (pp. 358-362). IEEE.
  • [12] Lee, W. K., & Song, K. M. (2018, August). Enhanced ISAR imaging for surveillance of multiple drones in urban areas. In 2018 International Conference on Radar (RADAR) (pp. 1-4). IEEE.
  • [13] Kim, K. T., Seo, D. K., & Kim, H. T. (2005). Efficient classification of ISAR images. IEEE Transactions on Antennas and Propagation, 53(5), 1611-1621.
  • [14] Sayed, A. N., Ramahi, O. M., & Shaker, G. (2024). In the Realm of Aerial Deception: UAV Classification via ISAR Images and Radar Digital Twins for Enhanced Security. IEEE Sensors Letters.
  • [15] Barbeau, M. (2019). Recognizing drone swarm activities: Classical versus quantum machine learning. Digitale Welt, 3(4), 45-50.
  • [16] Chen, V., & Martorella, M. (2014). Inverse synthetic aperture radar imaging: principles, algorithms and applications. IET. doi: 10.1049/SBRA504E
  • [17] “ANSYS HFSS SBR+” https://www.ansys.com/content/dam/resource-center/application-brief/ansys-sbr-plus.pdf (accessed: Dec 18, 2023).
  • [18] Ketkar, N., Moolayil, J., Ketkar, N., & Moolayil, J. (2020). Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. Apress LP.
  • [19] Alkouz, B., Abusafia, A., Lakhdari, A., & Bouguettaya, A. (2022). In-flight energy-driven composition of drone swarm services. IEEE Transactions on Services Computing, 16(3), 1919-1933.
  • [20] Adoni, W. Y. H., Lorenz, S., Fareedh, J. S., Gloaguen, R., & Bussmann, M. (2023). Investigation of autonomous multi-UAV systems for target detection in distributed environment: Current developments and open challenges. Drones, 7(4), 263.
  • [21] Alkouz, B., & Bouguettaya, A. (2020, December). Formation-based selection of drone swarm services. In MobiQuitous 2020-17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (pp. 386-394). doi: 10.1145/3448891.3448899

ISAR Görüntülemesi kullanılarak Drone Sürüsü Sınıflandırması

Yıl 2024, Cilt: 5 Sayı: 2, 127 - 134
https://doi.org/10.53525/jster.1529575

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

Kaynakça

  • [1] Coluccia, A., Parisi, G., & Fascista, A. (2020). Detection and classification of multirotor drones in radar sensor networks: A review. Sensors, 20(15), 4172. doi: 10.3390/s20154172
  • [2] Tang, J., Duan, H., & Lao, S. (2023). Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: A comprehensive review. Artificial Intelligence Review, 56(5), 4295-4327. doi: 10.1007/s10462-022-10281-7
  • [3] Ciattaglia, G., Temperini, G., Spinsante, S., & Gambi, E. (2021, June). mmWave Radar Features Extraction of Drones for Machine Learning Classification. In 2021 IEEE 8th International Workshop on Metrology for AeroSpace (MetroAeroSpace) (pp. 259-264). IEEE. doi: 10.1109/MetroAeroSpace51421.2021.9511703
  • [4] Chen, V. C. (2019). The micro-Doppler effect in radar. Artech house.
  • [5] Franceschetti, G., & Lanari, R. (2018). Synthetic aperture radar processing. CRC press.
  • [6] Gökdoğan, B. Y., Çoruk, R. B., Aydın, E., & Kara, A. 2D Millimeter-Wave SAR Imaging with Automotive Radar. Journal of Science, Technology and Engineering Research, 5(1), 68-77.
  • [7] Borkar, V. G., Ghosh, A., Singh, R. K., & Chourasia, N. (2010). Radar cross-section measurement techniques. Defence Science Journal, 60(2), 204-212. doi: 10.14429/dsj.60.341
  • [8] Yang, Y., Wang, X. S., Li, Y. Z., & Shi, L. F. (2019, September). RCS measurements and ISAR images of fixed-wing UAV for fully polarimetric radar. In 2019 International Radar Conference (RADAR) (pp. 1-5). IEEE. doi: 10.1109/RADAR41533.2019.171361
  • [9] Li, C. J., & Ling, H. (2016). An investigation on the radar signatures of small consumer drones. IEEE Antennas and Wireless Propagation Letters, 16, 649-652.
  • [10] Yang, Y., Wang, X. S., Li, Y. Z., & Shi, L. F. (2019, September). RCS measurements and ISAR images of fixed-wing UAV for fully polarimetric radar. In 2019 International Radar Conference (RADAR) (pp. 1-5). IEEE.
  • [11] Hamad, A., & Berens, P. (2024, July). 3D ISAR Imaging of an in-Air Rotating Drone Using Sparse Recovery and Multi-Channel Interferometry. In 2024 International Radar Symposium (IRS) (pp. 358-362). IEEE.
  • [12] Lee, W. K., & Song, K. M. (2018, August). Enhanced ISAR imaging for surveillance of multiple drones in urban areas. In 2018 International Conference on Radar (RADAR) (pp. 1-4). IEEE.
  • [13] Kim, K. T., Seo, D. K., & Kim, H. T. (2005). Efficient classification of ISAR images. IEEE Transactions on Antennas and Propagation, 53(5), 1611-1621.
  • [14] Sayed, A. N., Ramahi, O. M., & Shaker, G. (2024). In the Realm of Aerial Deception: UAV Classification via ISAR Images and Radar Digital Twins for Enhanced Security. IEEE Sensors Letters.
  • [15] Barbeau, M. (2019). Recognizing drone swarm activities: Classical versus quantum machine learning. Digitale Welt, 3(4), 45-50.
  • [16] Chen, V., & Martorella, M. (2014). Inverse synthetic aperture radar imaging: principles, algorithms and applications. IET. doi: 10.1049/SBRA504E
  • [17] “ANSYS HFSS SBR+” https://www.ansys.com/content/dam/resource-center/application-brief/ansys-sbr-plus.pdf (accessed: Dec 18, 2023).
  • [18] Ketkar, N., Moolayil, J., Ketkar, N., & Moolayil, J. (2020). Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. Apress LP.
  • [19] Alkouz, B., Abusafia, A., Lakhdari, A., & Bouguettaya, A. (2022). In-flight energy-driven composition of drone swarm services. IEEE Transactions on Services Computing, 16(3), 1919-1933.
  • [20] Adoni, W. Y. H., Lorenz, S., Fareedh, J. S., Gloaguen, R., & Bussmann, M. (2023). Investigation of autonomous multi-UAV systems for target detection in distributed environment: Current developments and open challenges. Drones, 7(4), 263.
  • [21] Alkouz, B., & Bouguettaya, A. (2020, December). Formation-based selection of drone swarm services. In MobiQuitous 2020-17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (pp. 386-394). doi: 10.1145/3448891.3448899
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kablosuz Haberleşme Sistemleri ve Teknolojileri (Mikro Dalga ve Milimetrik Dalga dahil)
Bölüm Araştırma Makaleleri
Yazarlar

Remziye Büşra Çoruk 0000-0002-9466-3862

Ali Kara 0000-0002-9739-7619

Elif Aydın 0000-0001-6878-1796

Yayımlanma Tarihi
Gönderilme Tarihi 7 Ağustos 2024
Kabul Tarihi 23 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: 2

Kaynak Göster

APA Çoruk, R. B., Kara, A., & Aydın, E. (t.y.). 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 Çoruk RB, Kara A, Aydın E. Drone Swarm Classification from ISAR Imaging. JSTER. 5(2):127-134. doi:10.53525/jster.1529575
Chicago Çoruk, Remziye Büşra, Ali Kara, ve Elif Aydın. “Drone Swarm Classification from ISAR Imaging”. Journal of Science, Technology and Engineering Research 5, sy. 2 t.y.: 127-34. https://doi.org/10.53525/jster.1529575.
EndNote Çoruk RB, Kara A, Aydın E Drone Swarm Classification from ISAR Imaging. Journal of Science, Technology and Engineering Research 5 2 127–134.
IEEE R. B. Çoruk, A. Kara, ve E. Aydın, “Drone Swarm Classification from ISAR Imaging”, JSTER, c. 5, sy. 2, ss. 127–134, doi: 10.53525/jster.1529575.
ISNAD Çoruk, Remziye Büşra vd. “Drone Swarm Classification from ISAR Imaging”. Journal of Science, Technology and Engineering Research 5/2 (t.y.), 127-134. https://doi.org/10.53525/jster.1529575.
JAMA Çoruk RB, Kara A, Aydın E. Drone Swarm Classification from ISAR Imaging. JSTER.;5:127–134.
MLA Çoruk, Remziye Büşra vd. “Drone Swarm Classification from ISAR Imaging”. Journal of Science, Technology and Engineering Research, c. 5, sy. 2, ss. 127-34, doi:10.53525/jster.1529575.
Vancouver Çoruk RB, Kara A, Aydın E. Drone Swarm Classification from ISAR Imaging. JSTER. 5(2):127-34.
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