Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Automatic Recognition Algorithm of Unknown Ships on Radar

레이더 상 불특정 선박의 자동식별 알고리즘

  • 정현철 (국방대학교 컴퓨터공학) ;
  • 윤성웅 (국방대학교 컴퓨터공학) ;
  • 이상훈 (국방대학교 컴퓨터공학과)
  • Received : 2015.09.15
  • Accepted : 2016.04.30
  • Published : 2016.08.15
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Abstract

Seeking and recognizing maritime targets are very important tasks for maritime safety. While searching for maritime targets using radar is possible, recognition is conducted without automatic identification system, radio communicator or visibility. If this recognition is not feasible, radar operator must tediously recognize maritime targets using movement features on radar base on know-how and experience. In this paper, to support the radar operator's mission of continuous observation, we propose an algorithm for automatic recognition of an unknown ship using movement features on radar and a method of detecting potential ship related accidents. We extract features from contact range, course and speed of four types of vessels and evaluate the recognition accuracy using SVM and suggest a method of detecting potential ship related accidents through the algorithm. Experimentally, the resulting recognition accuracy is found to be more than 90% and presents the possibility of detecting potential ship related accidents through the algorithm using information of MV Sewol. This method is an effective way to support operator's know-how and experience in various circumstances and assist in detecting potential ship related accidents.

해상 안전을 위한 선박의 탐색 및 식별은 매우 중요하다. 선박의 탐색은 레이더로 가능하나, 식별은 선박자동식별장치, 통신장비, 시각 등에 의해 이루어지며, 이러한 식별수단이 불능 시 레이더 운용자의 경험과 지식을 바탕으로 선박의 기동특성을 참고하여 식별하는 매우 어려운 경우가 발생한다. 본 논문에서는 지속적인 관찰임무를 수행해야 할 선박 탐색요원의 임무를 보조하기 위하여 레이더 상 선박의 기동특성을 이용, 자동식별 및 사고발생 가능성을 탐지하는 방법을 제안한다. 4가지 유형의 선박 정보, 레이더 상 접촉거리 및 침로, 속력을 이용하여 그 특징을 추출하고, SVM을 활용하여 식별 정확도를 평가하였으며, 이를 이용한 자동식별 알고리즘을 통해 사고발생 가능성이 있는 선박을 선별하는 방법을 제시하였다. 실험 결과 90% 이상의 식별 정확도를 보였으며, 실제 사고선박인 세월호의 정보를 자동식별 알고리즘에 적용하여 선별 가능함을 보였다. 이 방법은 다양한 상황에서 선박 탐색요원의 경험과 지식을 효과적으로 보완하고, 다수의 선박 중 관심필요선박을 사전 식별하여 정보를 제공함으로서 탐색요원의 노력을 경감시키고, 문제점을 보다 빨리 인지하는데 도움이 될 것이다.

Keywords

References

  1. Choi in-Sik, "Technology trend and prosoect of Radar Target Recognition," Journal of the Korea Institute of Information and Communication Engineering, Vol. 13, No. 2, pp. 34-40, 2012.
  2. Kim Hak-Yeoun, "Radar's development trend and The development way of sea surveillance radar," Journal of JCS, Vol. 42, pp. 71-75, 2010.
  3. Martin, J. and Mulgrew, B., "Analysis of the Effect of Blade Pitch on the Return Signal from Rotating Aircraft Blades," IEEE Radar 92 International Conference, Brighton, UK, pp. 446-449, 1992.
  4. Bell, M. R., andd Grubbs, R. A., "JEM Modelling and Measurement for Radar Target Identification," IEEE Transactions on Aerospace and Electronic System, Vol. 29, No. 1, pp. 73-87, 1993. https://doi.org/10.1109/7.249114
  5. Hamid Ghadaki, Reza Dizaji, "Target Track Classification For Airport Surveillance Radar(ASR)," Radar, IEEE Conference on, pp. 136-139, Apr. 2006.
  6. Zhao, Q., Xu, D. X., and Principe, J., "Pose estimation of SAR automatic target recognition," Proc. of Image Understanding Workshop, Monterey, CA., pp. 827-832, Nov. 1998.
  7. Vladmir Vapnik., "Statistical learning theory," John Wiley Sone, New YORK, 1998.
  8. N. Cristianini and J. Shawe-Taylor, "An Introduction to Support Vector Machines and Other Kernelbased Learning Methods," Cambridge, U.K.:Cambridge Univ. Press, 2000.
  9. T. Hastie, R. Tibshirani, and J. Friedman, "The Elements of Statistical Learning," New York : Spinger-Verlag, 2001.
  10. Zang Li, Zhou Weida and Jiao Licheng, "Radar Target Recognition Based on Support Vector Machine," Signal Processing Proceedings, WCCC-ICSO 2000, 5th International Conference on, Vol. 3, pp. 1453-1456
  11. Jiajin Lei, Chao Lu, "Target Classification Based on Micro-Doppler Signatures," Radar Conference, 2005 IEEE International, pp. 179-183, May 2005.
  12. Zhao, Q., and Principe, J., "Support Vector Machines for SAR Automatic Target Recognition," IEEE Transactions on Aerospace and Electronic System, Vol. 37, No. 2, pp. 643-654, Apr. 2001. https://doi.org/10.1109/7.937475
  13. Youngwook Kim, Hao Ling, "Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 5, pp. 1328-1337, May 2009. https://doi.org/10.1109/TGRS.2009.2012849
  14. LIBSVM(A Library for Support Vector Machines), available at http://www.csie.ntu.edu.tw/-cjlin/libsvm