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http://dx.doi.org/10.5391/JKIIS.2011.21.4.469

Intelligent Maneuvering Target Tracking Based on Noise Separation  

Son, Hyun-Seung (연세대학교 전기전자공학과)
Park, Jin-Bae (연세대학교 전기전자공학과)
Joo, Young-Hoon (군산대학교 제어로봇공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.4, 2011 , pp. 469-474 More about this Journal
Abstract
This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.
Keywords
TS fuzzy system; acceleration; noise; filtering; maneuvering target tracking;
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1 Aristidis Likas, Nikos Vlassis, JakobJ. Verbeek, "The global k-means clustering algorithm", A. Likas et al. / Pattern Recognition, vol. 36, pp. 451-461, 2003.   DOI
2 Ohad Shamir, Naftali Tishby, "Stability and model selection in k-means clustering", Mach Learn, vol. 80, pp. 213-243, 2010.   DOI
3 T. P. Hong and C. Y. Leeb, "Induction of fuzzy rules and membership functions from training examples", Fuzzy Set and Systems, vol. 84, pp. 33-47, 1996.   DOI
4 D. Simon, "Training fuzzy systems with the extended Kalman filter", Fuzzy Sets and Systems, vol. 132, pp. 189-199, 2002.   DOI
5 Y. H. Joo, H. S. Hwang, K. B. Kim, and K. B. Woo, "Fuzzy system modeling by fuzzy partition and GA hybrid schemes", Fuzzy Sets and Systems, vol. 86, pp. 279-288, 1997.   DOI
6 H. A. P. Blom and Y. B. Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients", IEEE Trans. on Automatic Control, vol. 33, pp. 780-783, 1988.   DOI
7 S. Y. Noh, J. B. Park, and Y. H. Joo, "Intelligent tracking algorithm for maneuvering target using Kalman filter with fuzzy gain", IET Proceedings-Radar, Sonar and Navigation, vol. 1, No. 3, pp. 241-247, 2007.   DOI
8 A. Munir and D. P. Atherton, "Adaptive interacting multiple model algorithm for tracking a maneuvering target", IEE Proceedings-Radar, Sonar and Navigation, vol. 142, pp. 11-17, 1995.   DOI
9 Y. B. Shalom and K. Birmiwal, "Variable dimension filter for maneuvering target tracking", IEEE Trans. on Aerospace and Electronic Systems, vol. 18, pp. 621-629, 1982.   DOI
10 B. J. Lee, J. B. Park, and Y. H. Joo, "Fuzzy-logic-based IMM algorithm for tracking a maneuvering target", IEE Proceedings-Radar, Sonar and Navigation, vol. 152, No. 1, pp. 16-22, 2005.   DOI
11 McGinnity, S., and Irwin, G.W., "Fuzzy logic approach to maneuvering target tracking", IEE Proceedings-Radar, Sonar Navigation, vol. 145, No. 6, pp. 337-341, 1998, 6.   DOI
12 Hyun Seung Son, Jin Bae Park, and Young Hoon Joo, "Intelligent Maximum Noise-level Algorithm of Tracking the Maneuvering Target", Proceedings of KIIS Fall Conference 2010, vol. 20, no. 2, pp. 373-376, 2010.
13 Hyun Seung Son, Jin Bae Park, and Young Hoon Joo, "Target tracking Method by Acceleration extracting based on Fuzzy rule", Proceedings of KIIS Spring Conference 2011, vol. 21, no. 1, pp. 252-253, 2011.
14 Liu. Jianshu, He. Yajuan, Wang. Xiaoyong, Wu. Xiaozhou, Yang. Na, "A Fuzzy Adaptive Maneuvering Target Tracking Algorithm", Journal of Projectiles, Rockets, Missiles and Guidance, vol. 30, no. 4, pp. 8-10. July 2010.
15 B. Anderson and J. Moore, Optimal Filtering, Prentice-Hall, Englewood Cliffs, NJ, 1979.
16 R. A. Singer, "Estimating optimal tracking filter performance for manned maneuvering targets", IEEE Trans. Aerospace and Electronic Systems, vol. 4, pp. 473-483, 1970, 4.   DOI   ScienceOn
17 P. L. Bogler, "Tracking a maneuvering target using input estimation", IEEE Trans. on Aerospace and Electronic Systems, vol. 23, pp. 298-310, 1987.   DOI
18 Y. T. Chan, A. G. C. Hu, and J. B. Plant, "A Kalman filter based tracking scheme with input estimation", IEEE Trans. on Aerospace and Electronic Systems, vol. 15, pp. 237-244, 1979.   DOI   ScienceOn
19 G. A. Einicke and L. B. White, "Robust extended Kalman filtering", IEEE Trans. on Signal Processing, vol. 47, no. 9, pp. 2596-2599, 1999, 9.   DOI
20 E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, "Interacting multiple model methods in target tracking : a survey", IEEE Trans. on Aerospace and Electronic Systems, vol. 34, pp. 103-123, 1998.   DOI