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http://dx.doi.org/10.9766/KIMST.2018.21.1.047

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

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)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.21, no.1, 2018 , pp. 47-60 More about this Journal
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
Anti-tank Weapon; Computer Vision; Moving Target; Aiming Unit; Rotational Angular Velocity; Target Tracking; Aiming Error;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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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 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.
6 Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas, "Tracking-Learning-Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.7, pp. 1409-1422, 2012.
7 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.
8 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.   DOI
9 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.
10 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.   DOI
11 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.
12 Ozuysal, Mustafa, et al., "Fast Keypoint Recognition Using Random Ferns," IEEE Transactions on Pattern Analysis and Machine Intelligence 32.3, pp. 448-461, 2010.   DOI
13 Ross, David A., et al., "Incremental Learning for Robust Visual Tracking," International Journal of Computer Vision 77.1, pp. 125-141, 2008.   DOI