JOURNAL BROWSE
Search
Advanced SearchSearch Tips
The Threat List Acquisition Method in an Engagement Area using the Support Vector Machines
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
The Threat List Acquisition Method in an Engagement Area using the Support Vector Machines
Koh, Hyeseung;
  PDF(new window)
 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
Threat List;Classification;Support Vector Machines(SVM);
 Language
Korean
 Cited by
 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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.