한국항행학회논문지 (Journal of Advanced Navigation Technology)

  • 제28권5호
  • /
  • Pages.616-622
  • /
  • 2024
  • /
  • 1226-9026(pISSN)
  • /
  • 2288-842X(eISSN)
한국항행학회 (The Korean Navigation Institute)
권호 표지
DOI QR코드

DOI QR Code

GPU를 이용한 우주감시레이다 신호처리 고속화 방안

High-Speed Signal Processing Using GPU for Space Surveillance Radar

  • 조인철 (LIG넥스원 레이다연구소) ;
  • 조성훈 (LIG넥스원 레이다연구소) ;
  • 안지훈 (LIG넥스원 레이다연구소) ;
  • 문현욱 (LIG넥스원 레이다연구소) ;
  • 손성환 (LIG넥스원 레이다연구소) ;
  • 정태희 (국방과학연구소 레이다전자전기술센터) ;
  • 임상호 (국방과학연구소 레이다전자전기술센터)
  • In-cheol Cho (Radar R&D Lab, LIG Nex1) ;
  • Sung-hoon Cho (Radar R&D Lab, LIG Nex1) ;
  • Ji-hoon An (Radar R&D Lab, LIG Nex1) ;
  • Hyun-wook Moon (Radar R&D Lab, LIG Nex1) ;
  • Sung-hwan Sohn (Radar R&D Lab, LIG Nex1) ;
  • Taehee Jeong (Radar and EW Technology Center, Agency for Defense Development (ADD)) ;
  • Sangho Lim (Radar and EW Technology Center, Agency for Defense Development (ADD))
  • 투고 : 2024.10.11
  • 심사 : 2024.10.30
  • 발행 : 2024.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 들어 각국이 경쟁적으로 우주개발에 막대한 자금을 투자하고 있고 수 만개의 인공위성 및 스파이 위성, 우주 잔해 등 우주 위험이 증가하는 추세다. 이에 따라 우주를 감시하기 위한 여러 자산이 있지만 상시 및 광역 감시가 가능한 레이다를 이용한 우주 물체 탐지에 대한 관심이 높아지고 있다. 기존의 레이다 시스템은 수 백키로 이내의 항공기, 함정, 미사일 등을 탐지하기 위해 운용했다면 우주 물체를 탐지하기 위한 레이다는 수천 키로까지 확장해 운용을 해야한다. 이러한 경우 신호처리를 해야 하는 데이터 크기가 증가하며 이는 레이다 운용에 지연을 초래할 수 있다. 본 논문에서는 레이다를 이용한 우주 물체 탐지시 GPU를 활용해 신호처리를 고속화하는 방법을 제안하고 CPU 대비 성능을 비교해 이를 검증하였다.

Recently, each country is competitively investing huge amounts of money in space development, and space risks such as tens of thousands of satellites, spy satellites, and space debris are increasing. Although there are several assets for monitoring space, interest is growing in the detection of space objects using radar, which is useful for constant alert and wide-area surveillance. While the existing radar system was operated to detect aircraft, ships, and missiles within hundreds of kilometers, radar to detect space objects must be expanded to thousands of kilometers. In this case, the size of data that impairs signal processing increases, which can lead to a decrease in radar detection performance. In this paper, we present a method of speeding up signal processing using GPU when detecting space objects, which are long-distance targets, and the results of comparing performance compared to existing CPUs.

키워드

과제정보

본 연구는 대한민국 정부 (산업통상자원부, 과학기술정보통신부 및 방위사업청) 재원으로 민군협력진흥원에서 수행하는 민군기술협력사업의 연구비 지원으로 수행되었습니다. (과제번호 23-CM-RS-12)

참고문헌

  1. C. Pardini and L. Anselmo, "Environmental sustainability of large satellite constellations in low earth orbit," Acta Astronautica, Vol. 170, pp. 27-36, May 2020, DOI: https://doi.org/10.1016/j.actaastro.2020.01.016.
  2. T. Schildknecht, "Optical surveys for space debris," Astronomy and Astrophysices Review, Vol. 14, No. 1, pp. 41-111, Jan. 2007, DOI: https://doi.org/10.1007/s00159-006-0003-9.
  3. J. Choi, C. Park, Y. Kim, H. Kim, J. Kwon and G. H. Kim, "A design study of signal processor for small tracking radar," The Journal of the Institute of Internet, Broadcasting and Communication, Vol. 20, Issue 5, pp. 71-77, Oct. 2020, DOI: https://doi.org/10.7236/JIIBC.2020.20.5.71.
  4. I. D. Gerg, D. C. Brown, S. G. Wagner, D. Cook, B. N. O'Donnell, T. Benson and T. C. Montgomery, "GPU acceleration for synthetic aperture sonar image reconstruction," in Global Oceans 2020: Singapore-US Gulf Coast, Biloxi: MS, pp. 1-9, Oct. 2020, DOI: https://doi.org/10.1109/IEEECONF38699.2020.9389388.
  5. S. S. Maddikonda and G. A. Shanmugha Sundaram, "SAR image processing using GPU," in 2014 International Conference on Communication and Signal Processing, Melmaruvathur: India, pp. 448-452, April 2014, DOI: https://doi.org/10.1109/ICCSP.2014.6949881.
  6. B. Liu, K. Wang, X. Liu and W. Yu, "An efficient SAR processor based on GPU via CUDA," in 2009 International Congress on Image and Signal Processing, Tianjin: China, pp. 1-5, Oct. 2009, DOI: https://doi.org/10.1109/CISP.2009.5304418.
  7. D. A. Zherdev, V. A. Procudin, E. Y. Minaev and V. A. Fursov, "HPC implementation of radar images modelling method using CUDA," Journal of Physics: Conference Series, Samara: Russia, p. 012083, April 2018, DOI: https://doi.org/10.1088/1742-6596/1096/1/012083.
  8. R. W. Linderman, J. Corner and S. Tucker, "Swathbuckler: real-time wide swath synthetic aperture radar image formation using embedded HPC," in 2006 HPCMP Users Group Conference, Denver: CO, pp. 244-251, June 2006, DOI: https://doi.org/10.1109/HPCMP-UGC.2006.68.
  9. M. G. Koltiska, B. Pierce, C. Maldonado, T. Quiller, M. McHarg and B. Bishop, "Utilizing cubesatellites for characterization of the AN/FSY-3 space fence system and other sensors," in Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference, Maui: HI, p. 100, Sept. 2017, Retrieved from https://amostech.com/TechnicalPapers/2017/Poster/Koltiska.pdf.
  10. G. Fonder, M. Hughes, M. Dickson, M. Schoenfeld and J. Gardner, "Space fence radar overview," in 2019 International Applied Computational Electromagnetics Society Symposium, Miami: FL, pp. 1-2, April 2019, Retrieved from https://ieeexplore.ieee.org/abstract/document/8712890.
  11. D. Luebke, M. Harris, N. Govindaraju, A. Lefohn, M. Houston, J. Owens, ..., I. Buck, "GPGPU: general-purpose computation on graphics hardware," in Proceedings of the 2006 ACM/IEEE conference on Supercomputing, Tampa: FL, pp. 208-es, Nov. 2006, DOI: https://doi.org/10.1145/1188455.1188672.
  12. J. Lee, "A study of the GPGPU performance," The Journal of the Institute of Internet, Broadcasting and Communication, Vol, 18, Issue 6, pp. 201-206, Dec. 2018, DOI: https://doi.org/10.7236/JIIBC.2018.18.6.201.
  13. D. Kirk, "NVIDIA CUDA software and GPU parallel computing architecture," in Proceedings of the 6th International Symposium on Memory Management, Montreal Quebec: Canada, pp. 103-104, Oct. 2007, DOI: https://doi.org/10.1145/1296907.1296909.
  14. H. S. Bae, S. H. Yu and H. Y. Kwon, "Data assimilation of real-time air quality forecast using CUDA," The Journal of the Institute of Internet, Broadcasting and Communication, Vol, 17, Issue 2, pp. 271-277, April 2017, DOI: https://doi.org/10.7236/JIIBC.2017.17.2.271.
  15. Y. I. Cho, C. L. Ha, J. H. Yang and J. H. Kim, "The design of parallel processing S/W using CUDA for realtime 3D laser ladar imaging system," Journal of the Korea Society of Computer and Information, Vol. 18, Issue 1, pp. 1-10, Jan. 2013, DOI: https://doi.org/10.9708/jksci.2013.18.1.001.
  16. A. Bachoo, "Using the CPU and GPU for real-time video enhancement on a mobile computer," in IEEE 10th International Conference on Signal Processing Proceedings, Beijing: China, pp. 405-408, Oct. 2010, DOI: https://doi.org/10.1109/ICOSP.2010.5657164.