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http://dx.doi.org/10.9766/KIMST.2018.21.6.877

An Efficient Search Strategy of Anti-Submarine Helicopter based on Multi-Static Operation in Furthest-On-Circles  

Kim, Changhyun (Department of Defense Science, National Defense University)
Oh, Rahgeun (Department of Marine Sciences and Convergent Technology, Hanyang University)
Kim, Sunhyo (Department of Marine Sciences and Convergent Technology, Hanyang University)
Choi, Jeewoong (Department of Marine Sciences and Convergent Technology, Hanyang University)
Ma, Jungmok (Department of Defense Science, National Defense University)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.21, no.6, 2018 , pp. 877-885 More about this Journal
Abstract
The anti-submarine helicopter is the most effective weapon system in anti-submarine warfare. Recently changes in the introduction of the anti-submarine warfare sonar system are expected to operate multi-static sonar equipment of the anti-submarine helicopter. Therefore, it is required to study the operational concept of multi-static of anti-submarine helicopter. This paper studies on the optimal search of multi-static based on anti-submarine helicopter considering Furthest On Circles(FOC). First, the deployment of the sensors of the anti-submarine helicopter is optimized using genetic algorithms. Then, the optimized model is extended to consider FOC. Finally, the proposed model is verified by comparing pattern-deployment the search method in Korean Navy.
Keywords
Multi-Static; Anti-Submarine Helicopter; Furthest On Circles; Genetic Algorithm;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 A. Washburn and M. Karatas, “Multistatic Search Theory,” Military Operations Research, Vol. 20, No. 1, pp. 21-38, 2015.
2 M. P. Fewell and S. Ozols, "Simple Detection- Performance Analysis of Multistatic Sonar for Anti- Submarine Warfare," Australian Government Department of Defence(DSTO-TR-2562), 2011.
3 P. Mcdowell, "Environmental and Statistical Performance Mapping Model for Underwater Acoustic Detection Systems," University of New Orleans Theses and Dissertations, 2010.
4 S. H. Kim, et. al., “Optimal Deployment of Sensor Nodes based on Performance Surface of Acoustic Detection,” Journal of the Korea Institute of Military Science and Technology, Vol. 18, No. 5, pp. 538- 547, 2015.   DOI
5 M. K. Cheon, et. al., “Optimal Search Pattern of Ships based on Performance Surface,” Journal of the Korea Institute of Military Science and Technology, Vol. 20, No. 3, pp. 328-336, 2017.   DOI
6 M. Mitchell, "An Introduction to Genetic Algorithm," MIT Press, 1998.
7 D. T. Pham and G. Jin, "Genetic Algorithm using Gradient-Like Reproduction Operator," Electron. Lett. 31st, pp. 1558-1559, 1995.
8 G. H. Hwang and W. T. Jang, "Advances in Evolutionary Algorithms," I-Tech Education and Publishing, Vienna, p. 95, 2008.
9 T. P. Hong and H. S. Wang, "A Dynamic Mutation Genetic Algorithm," in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Vol. 3, pp. 2000-2005, 1996.
10 C. Amante and B. W. Eakins, "ETOPO1 Arc-Minute Global Relief Model : Procedures, Data Source and Analysis," NOAA Technical Memorandum, NGDC (National Geophysical Data Center), 2009.
11 M. R. Carnes, "Description and Evaluation of GDEM-V 3.0," Naval Research Laboratory, 2009.
12 Korea Institute of Geology, Mining and Materials Rep. NP 2007-010, 2007.
13 B. P. Michael, “Gaussian Beam Tracing for Computing Ocean Acoustic Fields,” Journal of the Acoustical Society of America, Vol. 82, No. 4, pp. 1349-1359, 1987.   DOI