DOI QR코드

DOI QR Code

Intelligent Maneuvering Target Tracking Based on Noise Separation

잡음 구분에 의한 지능형 기동표적 추적기법

  • 손현승 (연세대학교 전기전자공학과) ;
  • 박진배 (연세대학교 전기전자공학과) ;
  • 주영훈 (군산대학교 제어로봇공학과)
  • Received : 2011.05.06
  • Accepted : 2011.07.29
  • Published : 2011.08.25

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.

본 논문에서는 기동표적의 위치 오차값 보상 기법을 이용한 지능형 기동표적 추적 기법을 제안한다. 기동표적의 관측값과 예상위치와의 차이를 가속도와 순수 잡음으로 분리한다. 최적의 수준으로 가속도를 추출하기 위하여 K-means 클러스터링 기법과 TS 퍼지 시스템을 이용한다. K-means 클러스터링에 의해 분리된 가속도와 잡음에 대한 소속함수를 설정하고 퍼지 모델화하여 기동표적의 특성을 파악한다. 계산상의 오차를 보상하기 위하여 분리된 가속도와 잡음은 추적 알고리즘의 계산과정에 적절히 이용된다. 추정값 계산시, 가속도를 분리 하므로써 필터링 과정은 표적의 비선형 기동을 선형기동으로 인식하여 칼만필터의 성능을 유지시킨다. 기동표적의 비선형성에 대한 오차는 추정된 가속도를 통해 보상된다. 제안된 시스템의 소속함수에 사용되는 파라미터값을 조종하여 상황에 따라 적응성과 강인성을 향상시킨다. 제안된 시스템은 실시간 추적이 가능하도록 구성하였으며, 몇 가지 예를 통하여 본 논문에서 제안한 방법의 우수성을 증명한다.

Keywords

References

  1. 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. https://doi.org/10.1109/TAES.1970.310128
  2. 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
  3. 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
  4. B. Anderson and J. Moore, Optimal Filtering, Prentice-Hall, Englewood Cliffs, NJ, 1979.
  5. 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. https://doi.org/10.1109/78.782219
  6. 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
  7. 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
  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. https://doi.org/10.1049/ip-rsn:19951528
  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. https://doi.org/10.1109/TAES.1982.309274
  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. https://doi.org/10.1049/ip-rsn:20041002
  11. 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. https://doi.org/10.1049/iet-rsn:20060030
  12. 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. https://doi.org/10.1049/ip-rsn:19982427
  13. 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.
  14. 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.
  15. 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.
  16. Aristidis Likas, Nikos Vlassis, JakobJ. Verbeek, "The global k-means clustering algorithm", A. Likas et al. / Pattern Recognition, vol. 36, pp. 451-461, 2003. https://doi.org/10.1016/S0031-3203(02)00060-2
  17. Ohad Shamir, Naftali Tishby, "Stability and model selection in k-means clustering", Mach Learn, vol. 80, pp. 213-243, 2010. https://doi.org/10.1007/s10994-010-5177-8
  18. 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. https://doi.org/10.1016/0165-0114(95)00305-3
  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. 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. https://doi.org/10.1016/S0165-0114(95)00414-9