Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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The Reduction Methodology of External Noise with Segmentalized PSO-FCM: Its Application to Phased Conversion of the Radar System on Board

축별 분할된 PSO-FCM을 이용한 외란 감소방안: 함정용 레이더의 위상변화 적용

  • Son, Hyun-Seung (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Park, Jin-Bae (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Joo, Young-Hoon (Dept. of Control and Robotics Engineering, Kunsan National University)
  • 손현승 (연세대학교 전기전자공학과) ;
  • 박진배 (연세대학교 전기전자공학과) ;
  • 주영훈 (군산대학교 제어로봇공학과)
  • Received : 2012.04.30
  • Accepted : 2012.06.20
  • Published : 2012.07.01
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Abstract

This paper presents an intelligent reduction method for external noise. The main idea comes from PSO-FCM (Particle Swam Optimization Fused fuzzy C-Means) clustering. The data of the target is transformed from the antenna coordinates to the vessel one and to the system coordinates. In the conversion, the overall noises hinder observer to get the exact position and velocity of the maneuvering target. While the filter is used for tracking system, unexpected acceleration becomes the main factor which makes the uncertainty. In this paper, the tracking efficiency is improved with the PSO-FCM and the compensation methodology. The acceleration is approximated from the external noise splitted by the proposed clustering method. After extracting the approximated acceleration, the rest in the noise is filtered by the filter and the compensation is added to after that. Proposed tracking method is applicable to the linear model and nonlinear one together. Also, it can do to the on-line system. Finally, some examples are provided to examine the reliability of the proposed method.

Keywords

References

  1. H. S. Son, J. B. Park, and Y. H. Joo, "SIMM method base on acceleration extraction for nonlinear maneuvering target tracking," Journal of Electrical Engineering and Technology, vol. 7, no. 2, pp. 255-263, 2012. https://doi.org/10.5370/JEET.2012.7.2.255
  2. R. A. Singer, "Estimating optimal tracking filter performance for manned maneuvering targets," IEEE Trans. Aerospace and Electronic Systems, vol. 4, pp. 473-483, 1970.
  3. P. L. Bogler, "Tracking a maneuvering target using input estimation," IEEE Trans. on Aerospace and Electronic Systems, vol. 23, pp. 298-310, 1987. https://doi.org/10.1109/TAES.1987.310826
  4. 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. https://doi.org/10.1109/TAES.1979.308710
  5. B. Anderson and J. Moore, Optimal Filtering, Prentice- Hall, Englewood Cliffs, NJ, 1979.
  6. G. A. Einicke and L. B. White, "Robust extended Kalman filtering," IEEE Trans. on Signal Processing, vol. 47, no. 9, pp. 2596-2599, 1999. https://doi.org/10.1109/78.782219
  7. 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. https://doi.org/10.1109/7.640267
  8. 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. https://doi.org/10.1109/9.1299
  9. 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. https://doi.org/10.1049/ip-rsn:19951528
  10. 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. https://doi.org/10.1109/TAES.1982.309274
  11. H. A. P. Blom, "An Efficient filter for abruptly changing systems," Proc. of the 23rd IEEE conf. Decision and Control, pp. 656-658, Las Vegas, NV, USA, Dec. 1984.
  12. H. A. P. Blom, "The interacting multiple model algorithm for systems with markovian switching coefficients," IEEE Trans. on Automatic cont. Syst., vol. 33, no. 8, pp. 780-783, 1988. https://doi.org/10.1109/9.1299
  13. 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 Trans. Aerosp. Electron. Syst., vol. 25, no. 2, pp. 296-300, 1989. https://doi.org/10.1109/7.18693
  14. D. P. Atherton and H. J. Lin, "Parallel implementation of IMM tracking algorithm using transputers," IEE Proceedings-Radar, Sonar and Navigation, vol. 141, pp. 325-332, 1994. https://doi.org/10.1049/ip-rsn:19941513
  15. 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. https://doi.org/10.1049/ip-rsn:19951528
  16. 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. https://doi.org/10.1049/ip-rsn:19982427
  17. H. S. Son, J. B. Park, and Y. H. Joo, "The study of clustering algorithm for external noise segmentalized by axis using PSO-FCM," Proc. of the 27th ICROS Annual Conference (in Korean), Seoul, Korea, Apr. 2012.
  18. J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE International Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
  19. D. Simon, "Training fuzzy systems with the extended Kalman filter," Fuzzy Sets and Systems, vol. 132, pp. 189-199, 2002. https://doi.org/10.1016/S0165-0114(01)00241-X
  20. B. S. Kim, S. K. Park, B. H. Choi, E. T. Kim, H. J. Lee, and H. J. Kang, "Collision risk assessment for pedestrians' safety using neural network," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 1, pp. 6-11, 2011. https://doi.org/10.5302/J.ICROS.2011.17.1.6
  21. D. W Kim, H. J. Lee, and J. N. Sur, "T-S Fuzzy model-based waypoints-tracking control of underwater vehicles," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 6, pp. 526-530, 2011. https://doi.org/10.5302/J.ICROS.2011.17.6.526
  22. W. T. Thomson, Introduction to Space Dynamics, John Willey & Sons, 1961.

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