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http://dx.doi.org/10.5302/J.ICROS.2014.13.1896

A Study on the Target Tracking Algorithm based on the Target Size Estimation  

Jung, Yun Sik (Daegu 2nd team, Defense Agency for Technology and Quality)
Lee, Sang Suk (Daegu 3nd team, Defense Agency for Technology and Quality)
Rho, Shin Baek (Daegu 2nd team, Defense Agency for Technology and Quality)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.20, no.1, 2014 , pp. 29-36 More about this Journal
Abstract
In this paper, a novel MBE (Model Based target size Estimator) is presented for SDIIR (Strap Down Imaging Infrared) seekers. The target tracking requires the target size information for which residual range between target and missile should be provided. Unfortunately, in general, the missile with passive sensor such as IIR (Imaging Infrared), CCD (Coupled Charging Device) cannot obtain range information. To overcome the problem, the proposed method enables the SDIIR seeker to estimates target size by using target size model and track the target. The performance of proposed method is tested at IIR target tracking of target intercept scenario. The experiment results show that the proposed algorithm has the relatively good performance.
Keywords
target tracking; target size; imaging infrared; distance information; HPDAF;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 L. Qi and Z. Shi, "A method for FLIR target tracking based on distance updating," 2008 Congress on Image and Signal Processing, May, 2008.
2 T. L. Song, D. G. Lee, and J. H. Ryu, "A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment," Signal Processing, vol. 85, no. 10, Oct. 2005.
3 E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall, 1998.
4 Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, Academic Press, New York, 1988.
5 Y. Bar-Shalom and X. R. Li, Estimation and Tracking: Principles and Techniques and Software, Artech House, Inc, 1993.
6 T. L. Song and D. G. Lee, "A probabilistic nearest neighbor filter algorithm for m validated measurements," IEEE Trans. on Signal Processing, Jul. 2006.
7 K. J. Rhee and T. L. Song, "A probabilistic strongest neighbor filter algorithm based on number of validated measurement," JSASS 16th International Sessions in the 40th aircraft symposium, Japan, Oct. 2002.
8 T. L. Song, Y. T. Lim, and D. G. Lee, "A probabilistic strongest neighbor filter algorithm for m validated measurements," IEEE Trans. on Aerospace and Electronic Systems, vol. 48, no. 4, pp. 431-442, Apr. 2009.
9 T. L. Song and D. S. Kim, "Highest probability data association for active sonar tracking," The 9th International Conference on Information Fusion, Jul. 2006.
10 Y. S. Jung and T. L. Song, "IIR Target Initiation and Tracking using the HPDAF with Feature Information," Journal of the KIMST (in Korean), vol. 11, no. 4, pp. 124-132, Jun. 2008.   과학기술학회마을
11 R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.
12 Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000.   DOI   ScienceOn