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A Study on the TMBE Algorithm with the Target Size Information
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 Title & Authors
A Study on the TMBE Algorithm with the Target Size Information
Jung, Yun Sik; Kim, Jin Hwan;
 
 Abstract
In this paper, the target size and model based target size estimator (TMBE) algorithm is presented for iimaging infrared (IIR) seeker. At the imaging seeker, target size information is important factor for accurate tracking. The model based target size estimator filter (MBEF) algorithm was proposed to estimate target size at imaging infrared seeker. But, the model based target size estimator filter algorithm need to know relative distance from the target. In order to overcome the problem, we propose target size and model based target size estimator filter (TMBEF) algorithm which based on the target size. The performance of proposed algorithm is tested at target intercept scenario. The experiment results show that the proposed algorithm has the accurate target size estimating performance.
 Keywords
target tracking;target size;distance information;MBE;TMBE;
 Language
Korean
 Cited by
 References
1.
Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, Academic Press, New York, 1988.

2.
Y. Bar-Shalom and X. R. Li, Estimation and Tracking: Principles and Techniques and Software, Artech House, Inc, 1993.

3.
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.

4.
T. L. Song and D. G. Lee, "A probabilistic nearest neighbor filter algorithm for m validated measurements," IEEE Trans. on Signal Processing, Jul. 2006.

5.
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.

6.
T. L. Song, Y. T. Lim, and D. G. Lee, "A probabilistic strongest neighbor filter algorithm for m validated measurements," IEEE Trans on AES, vol. 48, no. 4, pp. 431-442, Apr. 2009.

7.
T. L. Song and D. S. Kim, "Highest probability data association for active sonar tracking," The 9th International Conference on Information Fusion, Jul. 2006.

8.
Y. S. Jung and T. L. Song, "A study of IIR target detection and tracking with feature based HPDA," The Korea Institute of Military Science and Technology (in Korean), vol. 11, no. 4, pp. 124-132, Jun. 2008.

9.
Y. Jung, S. S. Lee, and S. B. Rho, "A study on the target tracking algorithm based on the target size estimation," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 1, Jan. 2014.

10.
Y. Jung and S. B. Rho, "A study on the resizable target size estimation method for imaging target tracking," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 8, Apr. 2014.