DOI QR코드

DOI QR Code

A Method for Eliminating Aiming Error of Unguided Anti-Tank Rocket Using Improved Target Tracking

향상된 표적 추적 기법을 이용한 무유도 대전차 로켓의 조준 오차 제거 방법

  • Song, Jin-Mo (Dept. of Sensor Systems, Defence R&D Center, Hanwha Corporation) ;
  • Kim, Tae-Wan (Dept. of Sensor Systems, Defence R&D Center, Hanwha Corporation) ;
  • Park, Tai-Sun (Dept. of Tracical Missile Systems, Defence R&D Center, Hanwha Corporation) ;
  • Do, Joo-Cheol (Dept. of Tracical Missile Systems, Defence R&D Center, Hanwha Corporation) ;
  • Bae, Jong-sue (Dept. of Project Management, Headquarter, Hanwha Corporation)
  • 송진모 ((주)한화/방산 종합연구소 핵심기술2팀) ;
  • 김태완 ((주)한화/방산 종합연구소 핵심기술2팀) ;
  • 박태선 ((주)한화/방산 종합연구소 전술체계팀) ;
  • 도주철 ((주)한화/방산 종합연구소 전술체계팀) ;
  • 배종수 ((주)한화/방산 본사 사업관리팀)
  • Received : 2017.08.22
  • Accepted : 2018.01.12
  • Published : 2018.02.01

Abstract

In this paper, we proposed a method for eliminating aiming error of unguided anti-tank rocket using improved target tracking. Since predicted fire is necessary to hit moving targets with unguided rockets, a method was proposed to estimate the position and velocity of target using fire control system. However, such a method has a problem that the hit rate may be lowered due to the aiming error of the shooter. In order to solve this problem, we used an image-based target tracking method to correct error caused by the shooter. We also proposed a robust tracking method based on TLD(Tracking Learning Detection) considering characteristics of the FCS(Fire Control System) devices. To verify the performance of our proposed algorithm, we measured the target velocity using GPS and compared it with our estimation. It is proved that our method is robust to shooter's aiming error.

Keywords

References

  1. Lupher, John Hancock, et al., "Precision Guided Firearm with Hybrid Sensor Fire Control," U.S. Patent No. 9,222,754, 29 Dec. 2015.
  2. J. W. Lee, J. Y. Kang, "Direction of Development of Anti-Tank Weapons for Infantry and Recommendations for R & D," Defense & Technology, 434, pp. 78-83, 2015. 4.
  3. Viola, Paul, and Michael Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, Vol. 1, IEEE, 2001.
  4. Chapelle, Olivier, Bernhard Scholkopf, and Alexander Zien, "Semi-supervised Learning," IEEE Transactions on Neural Networks 20.3, pp. 542-542, 2009.
  5. Ross, David A., et al., "Incremental Learning for Robust Visual Tracking," International Journal of Computer Vision 77.1, pp. 125-141, 2008. https://doi.org/10.1007/s11263-007-0075-7
  6. Kalal, Zdenek, Jiri Matas, and Krystian Mikolajczyk, "Pn Learning: Bootstrapping Binary Classifiers by Structural Constraints," Computer Vision and Pattern Recognition(CVPR), 2010 IEEE Conference on, IEEE, 2010.
  7. Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas, "Tracking-Learning-Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.7, pp. 1409-1422, 2012.
  8. J. M. Song, S. H. Lee, and J. S. Bae, "A Study for Small Target Tracking Using Online Learning," Proceedings of the 2015 Korean Institute of Military Science and Technology(KIMST) Autumn Conference, 2015.
  9. J. M. Song, S. H. Lee, and J. S. Bae, "A Study for Vision-based Estimation Algorithm of Moving Target Using Aiming Unit of Unguided Rocket," Journal of the Korea Institute of Military Science and Technology 20.3, pp. 315-328, 2017. https://doi.org/10.9766/KIMST.2017.20.3.315
  10. Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas, "Forward-backward Error: Automatic Detection of Tracking Failures," Pattern Recognition(ICPR), 2010 20th International Conference on, IEEE, 2010.
  11. Di Stefano, Luigi, Stefano Mattoccia, and Federico Tombari, "ZNCC-based Template Matching Using Bounded Partial Correlation," Pattern Recognition Letters 26.14, pp. 2129-2134, 2005. https://doi.org/10.1016/j.patrec.2005.03.022
  12. Zheng, Bin, et al. "Object Tracking Algorithm based on Combination of Dynamic Template Matching and Kalman Filter," Intelligent Human-Machine Systems and Cybernetics(IHMSC), 2012 4th International Conference on, Vol. 2. IEEE, 2012.
  13. Ozuysal, Mustafa, et al., "Fast Keypoint Recognition Using Random Ferns," IEEE Transactions on Pattern Analysis and Machine Intelligence 32.3, pp. 448-461, 2010. https://doi.org/10.1109/TPAMI.2009.23