Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
권호 표지
DOI QR코드

DOI QR Code

The Threat List Acquisition Method in an Engagement Area using the Support Vector Machines

SVM을 이용한 교전영역 내 위협목록 획득방법

  • Koh, Hyeseung (The 1st Research and Development Institute, Agency for Defense Development)
  • 고혜승 (국방과학연구소 제1기술연구본부)
  • Received : 2015.08.28
  • Accepted : 2016.02.26
  • Published : 2016.04.05
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a threat list acquisition method in an engagement area using the support vector machines (SVM). The proposed method consists of track creation, track estimation, track feature extraction, and threat list classification. To classify the threat track robustly, dynamic track estimation and pattern recognition algorithms are used. Dynamic tracks are estimated accurately by approximating a track movement using position, velocity and time. After track estimation, track features are extracted from the track information, and used to classify threat list. Experimental results showed that the threat list acquisition method in the engagement area achieved about 95 % accuracy rate for whole test tracks when using the SVM classifier. In case of improving the real-time process through further studies, it can be expected to apply the fire control systems.

Keywords

References

  1. 한학용, "패턴인식 개론: MATLAB 실습을 통한 입체적 학습", 한빛미디어, pp. 482-504, 2009.
  2. W. Chen, S. Hsu and H. Shen, "Application of SVM and ANN for Intrusion Detection," Computers and Operation Research, Vol. 32, pp. 2617-2634, 2005. https://doi.org/10.1016/j.cor.2004.03.019
  3. F. Colas and P. Brazdil, "Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks," Artificial Intelligence in Theory and Practice, Vol. 217, pp. 169-178, 2006. https://doi.org/10.1007/978-0-387-34747-9_18
  4. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, "Least Squares Support Vector Machines," World Scientific, Singapore, 2002.
  5. V. Vapnik, "The Nature of Statistical Learning Theory," Springer, Inc. NewYork, 1995.
  6. S. Chen, C. F. N. Cowan and P. M. Grant, "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks," IEEE Transactions on Neural Networks, Vol. 2, No. 2, pp. 302-309, 1991. https://doi.org/10.1109/72.80341
  7. R. Kohavi, "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection," Appears in the International Joint Conference on artificial Intelligence, Vol. 14, No. 2, pp. 1137-1145, 1995.