The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

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

FCM 클러스터링 기반 비선형 기동표적의 외란분석 알고리즘

  • 손현승 (연세대학교 전기전자공학과) ;
  • 박진배 (연세대학교 전기전자공학과) ;
  • 주영훈 (국립 군산대학교 제어로봇 공학과)
  • Received : 2011.09.23
  • Accepted : 2011.11.16
  • Published : 2011.12.01
Download PDF
⟨ Previous Next ⟩

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

References

  1. 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. https://doi.org/10.1115/1.3662552
  2. B. O. D. Anderson and J. B. Moore, Optimal filtering, Prentice-hall, Englewood Cliffs, NJ, 1979.
  3. 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. https://doi.org/10.1109/TAES.1970.310128
  4. 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. https://doi.org/10.1109/7.102704
  5. 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.
  6. 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.
  7. 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.
  8. 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. https://doi.org/10.1109/TAC.1970.1099359
  9. 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.
  10. 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. https://doi.org/10.1109/9.1299
  11. 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. https://doi.org/10.1109/7.18693
  12. 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. https://doi.org/10.1109/7.640267
  13. 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. https://doi.org/10.1049/ip-rsn:19941513
  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, no. 1, pp. 11-17, 1995. https://doi.org/10.1049/ip-rsn:19951528
  15. 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. https://doi.org/10.1049/ip-rsn:20041002
  16. 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. https://doi.org/10.1049/iet-rsn:20060030
  17. 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. https://doi.org/10.1109/TAES.2008.4655358
  18. J. C . B ezdek, P attern R ecog nition w ith Fuzzy Objective Function Algorithms, Plenum Press, New York 1981.

Cited by

  1. Sorting radar signal based on wavelet characteristics of wigner-ville distribution vol.30, pp.5, 2013, https://doi.org/10.1007/s11767-013-3072-8