JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A System Design Method of Mine Warfare Using Information for SONAR and MDV
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A System Design Method of Mine Warfare Using Information for SONAR and MDV
Kim, Jun-Young; Shin, Chang-Hong; Kim, Kyung-Hee;
  PDF(new window)
 Abstract
The naval mine is the explosives that are installed in the water in order to attack surface ships or submarines. So mine warfare is a very important component of naval operations. In this paper, first, understanding of the general concept about mine warfare. Second, introduce the mine hunting progress and mine sweeping progress. And then, suggest the system design method of mine counter measure warfare using several functions. The functions are mine area detection algorithm for side scan sonar image using Adaboost algorithm, and calculation to mine hunting progress rate and mine sweeping progress rate. And techniques that lead the mine disposal vehicle(MDV) to mine.
 Keywords
Mine Counter Measure;Mine Hunting;Mine Sweeping;Adaboost;CADCAC;
 Language
Korean
 Cited by
 References
1.
D. W. Kim, "Operation of tactical data-link between weapon systems and interoperability test and evaluation," in Proc. KICS Int. Conf. Commun. 2012 (KICS ICC 2012), pp. 452- 453, Yongpyong, Korea, Feb. 2012.

2.
H. S. Kim, "A study on the defence IT survey and the acquisition method of IT technology," in Proc. KICS Int. Conf. Commun. 2014 (KICS ICC 2014), pp. 495-496, Yongpyong, Korea, Jan. 2014.

3.
K. B. Kim, "On software reliability engineering process for weapon systems," J. KICS, vol. 26, no. 4, pp. 305-428, Mar. 2011. crossref(new window)

4.
K. H. Kim, "Performance analysis of navigation and sonar system for unmanned mine disposal system," J. Ships and Ocean Eng., vol. 51. pp. 47-55, Jun. 2011.

5.
P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Computer Vision and Pattern Recognition 2001(CVPR 2001), pp. 511-518, Kauai, USA, Dec. 2001.

6.
R. Lienhart and J. Maydt, "An extended set of Haar-like features for rapid object detection," Int. Conf. Image Process.(ICIP2002), pp. 900-903, Rochester, USA, Sept. 2002.

7.
R. Lienhart, A. Kuranov, and V. Pisarevsky, "Empirical analysis of detection cascades of boosted classifiers for rapid object detection," DAGM'03, 25th Pattern Recognition Symp., pp. 297-304, Madgeburg, Germany, Sept. 2003.