The Journal of Korean Institute of Electromagnetic Engineering and Science (한국전자파학회논문지)

The Korean Institute of Electromagnetic Engineering and Science (한국전자파학회)
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De-Noising of HRRP Using EMD for Improvement of Target Identification Performance

표적 식별 성능 향상을 위한 EMD를 이용한 HRRP의 잡음 제거 기법

  • 박준용 (국방과학연구소) ;
  • 이승재 (포항공과대학교 전자전기공학과) ;
  • 양은정 (국방과학연구소) ;
  • 김경태 (포항공과대학교 전자전기공학과)
  • Received : 2017.02.14
  • Accepted : 2017.04.11
  • Published : 2017.04.30
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Abstract

In this paper, we propose an efficient method to remove noise component contained in high resolution range profile(HRRP) to improve target identification performance. The proposed method can effectively eliminate the noise component using both the statistical characteristics of the noise component and EMD algorithm. Experimental results show that the proposed method can substantially improve the identification capability, removing the noise component effectively.

본 논문에서는 레이다 표적식별 성능을 향상시키기 위하여 고해상도 거리측면도(High Resolution Range Profile: HRRP)에 포함된 잡음을 효과적으로 제거하는 방법을 제안한다. 제안된 기법은 HRRP에 포함된 잡음의 통계적인 특성과 EMD(Empirical Mode Decomposition) 알고리즘을 이용하여 HRRP에 포함된 잡음을 효과적으로 제거한다. 잡음 제거 실험 결과에서는, 본 논문에서 제안한 기법이 잡음을 효과적으로 제거하면서, 표적 식별 성능을 크게 향상시키는 것을 수치적으로 확인할 수 있었다.

Keywords

References

  1. C. Ozedemir, Inverse Synthetic Aperture RADAR Imaging with MATLAB Algorithms, Wiley, 2012.
  2. A. Zyweck, R. E. Bogner, "Radar target classification of commercial aircraft", IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 2, 598-606, 1996. https://doi.org/10.1109/7.489504
  3. N. Padmaja, S. Varadarajan, and R. Swathi, "Signal processing of RADAR echoes using wavelets and Hilbert Hung transform", SIPIJ, vol. 2, no. 3, pp. 101-119, Sep. 2011. https://doi.org/10.5121/sipij.2011.2310
  4. 박준용, 이승재, 양은정, 김경태, "EMD를 이용한 HRRP의 잡음 제거 기법", 2015년도 한국전자파학회 종합학술대회 논문집, 25(1), p. 11, 2015년 11월.
  5. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shin, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. Royal Soc. London A, pp. 903-995, 1998.
  6. P. Flandrin, G. Rilling, and P. Goncalves, "Empirical mode decomposition as a filter bank", IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112-114, Feb. 2004. https://doi.org/10.1109/LSP.2003.821662
  7. J. Huang, J. Xie, F. Li, and L. Li, "A threshold denoising method based on EMD", Journal of Theoretical and Applied Information Technology, vol. 47, no. 1, pp. 419-424, Jan. 2013.
  8. A. O. B, J. C. Cexus, "Denoising via empirical mode decomposition", in Proc. ISCCSP 2006, 2006.
  9. Y. Kopsinis, S. Mclaughlin, "Development of EMD-based denoising methods inspired by wavelet thresholding", IEEE Transactions on Signal Processing, vol. 57, no. 4, Apr. 2009.
  10. D. L. Donoho, "De-noising by soft-thresholding", IEEE Trans. Inform. Theory, pp. 613-627, 1995.
  11. Z. Wu, N. E. Huang, "A study of the characteristics of white noise using the empirical mode decomposition method", Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, pp. 1597- 1611, Jun. 2004.
  12. M. P. Fitz, Fundamentals of Communications Systems, McGraw-Hill, 2007.
  13. M. Soumekh, Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, John Wiley Inc, pp. 7-46, New York, 1999.
  14. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd, Wiley, 2001.
  15. D. Baleanu, "Advances in wavelet theory and their applications in engineering", Physics and Technology, In- Tech, 2012.
  16. B. P. Lathi, Linear Systems and Signals 2nd, Oxford, 2009.

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