Skip to main content
SpringerLink
Log in

Segmentalized FCM-based tracking algorithm for zigzag maneuvering target

  • Regular Paper
  • Intelligent and Information Systems
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

This paper presents a new method for tracking a zigzag maneuvering target by compensating for the positional error of the target. The positional difference between the measured and predicted points is separated into acceleration and noise. Fuzzy c-means (FCM) clustering is utilized as an adaptive method for noise separation. Approximating acceleration is determined by the membership function of the FCM. The approximated acceleration is used to compensate for the tracking error. The 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. D. P. Atherton, Stability of Nonlinear Systems, John Wiley, New York, 1981.

    MATH  Google Scholar 

  2. D. P. Atherton, Nonlinear Control Engineering, Student Edition, Van Nostrand Reinhold, London, 1982.

    MATH  Google Scholar 

  3. L. O. Chua, C. A. Desoer, and E. S. Kuh, Linear and Nonlinear Circuits, McGraw-Hill, New York, 1987.

    MATH  Google Scholar 

  4. R. A. Singer, “Estimating optimal tracking filter performance for manned maneuvering targets,” IEEE Trans. Aerospace and Electronic Syst., vol. 4, pp. 473–483, 1970.

    Article  Google Scholar 

  5. Y. T. Chan, A. G. C. Hu, and J. B. Plant, “A Kalman filter based tracking scheme with input estimation,” IEEE Trans. Aerospace and Electronic Syst., vol. 15, pp. 237–244, 1979.

    Article  Google Scholar 

  6. B. J. Lee, J. B. Park, and Y. H. Joo, “An intelligent tracking method for a maneuvering target,” International Journal of Control, Automation, and Systems, vol. 1, no. 1, pp. 93–100, 2003.

    Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. H. S. Son, J. B. Park, and Y. H. Joo, “SIMM method based on acceleration extraction for nonlinear maneuvering target tracking,” Journal of Electrical Engineering and Technology, vol. 7, no. 2, pp. 255–263, 2012.

    Article  Google Scholar 

  10. M. B. Ignagni, “Separate-bias Kalman estimator with bias state noise,” IEEE Trans. on Automatic Control, vol. 35, pp. 338–341, 1990.

    Article  MATH  Google Scholar 

  11. A. T. Alouani, P. Xia, T. R. Rice, and W. D. Blair, “A two-stage Kalman estimator for state estimation in the presence of random bias and for tracking a maneuvering targets,” Proc. of 30th IEEE Conference on Decision and Control, Brighton, England, 1991, pp. 2059–2062.

    Google Scholar 

  12. P. L. Bogler, “Tracking a maneuvering target using input estimation,” IEEE Trans. Aerospace and Electronic Syst., vol. 23, pp. 298–310, 1987.

    Article  Google Scholar 

  13. H. A. P. Blom and Y. B. Shalom, “The interacting multiple model algorithm for systems with Markovian switching coefficients,” IEEE Trans. Automatic Cont., vol. 33, pp. 780–783, 1988.

    Article  MATH  Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. S. McGinnity and G. W. Irwin, “Fuzzy logic approach to maneuvering target tracking,” IEE Proceedings-Radar, Sonar Navigation, vol. 145, no. 6, pp. 337–341, 1998.

    Article  Google Scholar 

  16. D. Simon, “Training fuzzy systems with the extended Kalman filter,” Fuzzy Sets and Syst., vol. 132, pp. 189–199, 2002.

    Article  MATH  Google Scholar 

  17. R. Mikut, O. Burmeister, L. Groll, and M. Reischl, “Takagi-Sugeno-Kang fuzzy classifiers for a special class of time-varying systems,” IEEE Trans. Fuzzy Syst., vol. 16, no. 4, pp. 1038–1049, 2008.

    Article  Google Scholar 

  18. C. T. Lin and C. S. George Lee, “Neural-network-based fuzzy logic control and decision system,” IEEE Trans. Fuzzy Syst., vol. 40, no. 12, pp. 1320–1336, 1991.

    Google Scholar 

  19. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, vol. 10, no. 2–3, pp. 191–203, 1984.

    Article  Google Scholar 

  20. R. L. Cannon, J. V. Dave, and J. C. Bezdek, “Efficient implementation of the fuzzy c-means clustering algorithms,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, no. 2, pp. 248–255, 1986.

    Article  MATH  Google Scholar 

  21. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Boston, MA, 1989.

    MATH  Google Scholar 

  22. M. Gen and R. Cheng, Genetic Algorithms and Engineering Design, John Wiley and Sons, New York, 1997.

    Google Scholar 

  23. H. S. Son, J. B. Park, and Y. H. Joo, “Fuzzy cmeans clustering-based smart tracking for three dimensional maneuvering target including unknown acceleration input,” IET Proceedings-Radar, Sonar and Navigation, vol. 7, pp. 623–634, 2013.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-749, Korea

    Hyun Seung Son & Jin Bae Park

  2. Department of Control and Robot Engineering, Kunsan National University, Gunsan, Jeonbuk, 573-701, Korea

    Young Hoon Joo

Authors
  1. Hyun Seung Son
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Bae Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young Hoon Joo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Bae Park.

Additional information

Hyun Seung Son received his B.S. from R.O.K Naval Academy in 2000 and his M.S. and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 2007 and 2014, respectively. He is currently a naval officer with R.O.K Navy. His major interests are in the fields of robust control and filtering, target tracking, fuzzy logic control, and nonlinear control, and optimization algorithms.

Jin Bae Park received his B.S. degree in Electrical Engineering from Yonsei University, Seoul, Korea in 1977 and his M.S. and Ph.D. degrees in Electrical Engineering from Kansas State University, Manhattan, Kansas, in 1985 and 1990, respectively. Since 1992, he has been with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, where he is currently a professor. His major interests are in the fields of robust control and filtering, nonlinear control, intelligent mobile robot, fuzzy logic control, neural networks, Hadamard transform, chaos theory, and genetic algorithms. Dr. Park served as the Editor-in-Chief (2006–2010) for the International Journal of Control, Automation, and Systems and the President (2013) for the Institute of Control, Robot, and Systems Engineers (ICROS). He is currently serving as the Senior Vice President for Yonsei University.

Young Hoon Joo received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 1982, 1984, and 1995, respectively. He worked with Samsung Electronics Company, Seoul, Korea, from 1986 to 1995, as a project manager. He was with the University of Houston, Houston, TX, from 1998 to 1999, as a visiting professor in the Department of Electrical and Computer Engineering. He is currently a professor in the Department of Control and Robot Engineering, Kunsan National University, Korea. His major interest is mainly in the field of intelligent robot, intelligent control, human-robot interaction, and wind farm control. He served as President for Korea Institute of Intelligent Systems (KIIS) (2008–2009) and is serving as Editor-in-Chief for the International Journal of Control, Automation, and Systems (IJCAS) (2014-present) and is serving as the Vice-President for the Korean Institute of Electrical Engineers (KIEE) (2013-present).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Son, H.S., Park, J.B. & Joo, Y.H. Segmentalized FCM-based tracking algorithm for zigzag maneuvering target. Int. J. Control Autom. Syst. 13, 231–237 (2015). https://doi.org/10.1007/s12555-013-0406-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-013-0406-0

Keywords

Search

Navigation