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http://dx.doi.org/10.5370/KIEE.2011.60.12.2346

External Noise Analysis Algorithm based on FCM Clustering for Nonlinear Maneuvering Target  

Son, Hyun-Seung (연세대학교 전기전자공학과)
Park, Jin-Bae (연세대학교 전기전자공학과)
Joo, Young-Hoon (국립 군산대학교 제어로봇 공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.60, no.12, 2011 , pp. 2346-2351 More about this Journal
Abstract
This paper presents the intelligent external noise analysis method for nonlinear maneuvering target. After recognizing maneuvering pattern of the target by the proposed method, we track the state of the target. The external noise can be divided into mere noise and acceleration using only the measurement. divided noise passes through the filtering step and acceleration is punched into dynamic model to compensate expected states. The acceleration is the most deterministic factor to the maneuvering. By dividing, approximating, and compensating the acceleration, we can reduce the tracking error effectively. We use the fuzzy c-means (FCM) clustering as the method to divide external noise. FCM can separate the acceleration from the noise without criteria. It makes the criteria with the data made by measurement at every sampling time. So it can show the adaptive tracking result. The proposed method proceeds the tracking target simultaneously with the learning process. Thus it can apply to the online system. The proposed method shows the remarkable tracking result on the linear and nonlinear maneuvering. Finally, some examples are provided to show the feasibility of the proposed algorithm.
Keywords
Fuzzy c-means (FCM) clustering; Acceleration; noise; Maneuvering target tracking;
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  • Reference
1 Y. Bar-Shalom and K. Birmiwal, "Variable dimension filter for maneuvering target tracking", IEEE Transactions on Aerospace and Electronic Systems, vol. 18, no. 5, pp. 621-629, 1982.
2 G. A. Ackerson and K. S. Fu, "On state estimation in switching environments", IEEE Transactions on Automatic Control, vol. 15, no. 1, pp. 10-17, 1970.   DOI
3 P. L. Bogler, "Tracking a maneuvering target using input estimation", IEEE Transactions on Aerospace and Electronic Systems, vol. 23, no. 3, pp. 298-310, 1987.
4 D. P. Atherton and H. J. Lin, "Parallel implementation of IMM tracking algorithm using transputers", IEE Proceedings-Radar, Sonar and Navigation, vol. 141, no. 6, pp. 325-332, 1994.   DOI   ScienceOn
5 P. Gutman and V. Mordekhai "Tracking targets using adaptive Kalman filtering", IEEE Transactions on Aerospace and Electronic Systems, vol. 26, no. 5, pp. 691-698, 1990.   DOI   ScienceOn
6 Y. T. Chan, A. G. C. Hu, and J. B. Plant, "A Kalman filter based tracking scheme with input estimation", IEEE Transactions on Aerospace and Electronic Systems, vol. 15, no. 2, pp. 237-244, 1979.
7 Kalman, R. E., "A new approach to linear filtering and prediction problems", J. Basic Eng., Transactions ASME, series D. vol. 82, no. 1, march 1960, pp.35-45.   DOI
8 B. O. D. Anderson and J. B. Moore, Optimal filtering, Prentice-hall, Englewood Cliffs, NJ, 1979.
9 Singer, R. A. "Estimating optimal tracking filter performance for manned maneuvering targets", IEEE Transactions Aerospace and Electronic Systems, AES-6, no. 4, pp. 473-483, 1970.   DOI   ScienceOn
10 J. C . B ezdek, P attern R ecog nition w ith Fuzzy Objective Function Algorithms, Plenum Press, New York 1981.
11 A. Munir and D. P. Atherton, "Adaptive interacting multiple model algorithm for tracking a maneuvering target", IEE Proceedings-Radar, Sonar and Navigation, vol. 142, no. 1, pp. 11-17, 1995.   DOI   ScienceOn
12 B. J. Lee, J. B. Park, and Y. H. Joo, "Fuzzy logicbased IMM algorithm for tracking a manoeuvering target", IEE Proceedings Radar, Sonar and Navigation, vol. 152, no. 1, pp. 16-22, 2005.   DOI   ScienceOn
13 S. Y. Noh, J. B. Park, and Y. H. Joo, "Intelligent tracking algorithm for manoeuvering target using Kalman filter with fuzzy gain", IET Proceedings- Radar, Sonar and Navigation, vol. 1, no. 3, pp. 241-247, 2007.   DOI   ScienceOn
14 Y. Bar-Shalom, K. C. Chang, and H. A. P. Blom, "Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm", IEEE Transactions on Aerospace and Electronic Systems, vol. 25, no. 2, pp. 296-300, 1989.   DOI   ScienceOn
15 R. W. Osborne, III, Y. Bar-shalom, and T. Kirubarajan, "Radar measurement noise variance estimation with several targets of opportunity", IEEE Transactions on Aerospace and Electronic Systems, vol. 44, no. 3, pp. 985-995, 2008.   DOI   ScienceOn
16 C. B. Chang and M. Athans, "State estimation for discrete system with switching parameters", IEEE Transactions on Aerospace and Electronic Systems, vol. 14, no. 3, pp. 418-425, 1978.
17 H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients", IEEE Transactions on Automatic Control, vol. 33, no. 8, pp. 780-783, 1988.   DOI   ScienceOn
18 E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, "Interacting multiple model methods in target tracking : a survey", IEEE Transactions on Aerospace and Electronic Systems, vol. 34, no. 1, pp. 103-123, 1998.   DOI   ScienceOn