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

  • 제28권10호
  • /
  • Pages.825-831
  • /
  • 2017
  • /
  • 1226-3133(pISSN)
  • /
  • 2288-226X(eISSN)
한국전자파학회 (The Korean Institute of Electromagnetic Engineering and Science)
권호 표지
DOI QR코드

DOI QR Code

Cell under Test 데이터만을 이용한 사전정보 기반의 클러터 억제 알고리즘

Knowledge-Based Clutter Suppression Algorithm Using Cell under Test Data Only

  • Jeon, Hyeonmu (Department of Electronics Convergence Engineering, Kwangwoon University) ;
  • Yang, Dong-Hyeuk (Department of Defense Acquisition Program, Kwangwoon University) ;
  • Chung, Yong-Seek (Department of Electronics Convergence Engineering, Kwangwoon University) ;
  • Chung, Won-zoo (Department of Radio Communications Engineering, Korea University) ;
  • Kim, Jong-mann (Agency of Defence Development) ;
  • Yang, Hoon-Gee (Department of Electronics Convergence Engineering, Kwangwoon University)
  • 투고 : 2017.09.19
  • 심사 : 2017.10.16
  • 발행 : 2017.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

실제 레이다가 운용되는 환경에서 발생되는 클러터는 비균질성(heterogeneous)의 특성을 갖는 동시에 바이스태틱 레이다나 모노스태틱 non-sidelooking 레이다 구조인 경우는 클러터의 비정상성(nonstationary) 특성도 갖는다. 이러한 특성에 의해서 클러터 신호를 추정하는데 필요한 IID(Independent Identically Distributed) secondary 데이터 개수에 제약이 따르므로 클러터 억제 성능이 저하된다. 본 논문에서는 바이스태틱 레이다 환경에서 Cell under test에 대한 사전정보만을 이용하여 클러터 신호를 추정함으로써 secondary 데이터 없이 클러터를 억제하는 알고리즘을 제시한다. 바이스태틱 클러터의 angle-Doppler 스펙트럼 상에서 구조 분석을 통해 사전정보로 부터 클러터를 추정하는 것이 가능함을 보이고, 고유치 해석에 의해 클러터 억제 과정을 제시한다. 마지막으로 시뮬레이션을 통해 제시하는 클러터 억제 알고리즘의 성능을 보인다.

Radar clutter in real environment is in general heterogeneous and especially nonstationary if radar geometry is of non-sidelooking monostatic structure or bistatic structure. These clutter properties lead to the insufficient number of secondary data of IID(Independent identically distributed) property, conclusively deteriorate clutter suppression performance. In this paper, we propose a clutter suppression algorithm that estimates the clutter signal belonging to cull under test via calculation using only prior information, rather than using the secondary data. Through analyzing the angle-Doppler spectrum of the clutter signal, we show the estimation of the clutter signal using prior information only is possible and present the derivation of a clutter suppression algorithm through eigen-value analysis. Finally, we show the performance of the proposed algorithm by simulation.

키워드

참고문헌

  1. W. L. Melvin, "A STAP overview", IEEE Aerosp. Electron. Syst. Mag. vol. 19, no. 1, pp. 19-35, 2004. https://doi.org/10.1109/MAES.2004.1263229
  2. J. R. Guerci, Space-Time Adaptive Processing for Radar,/MA: Artech House, 2003.
  3. I. S. Reed, J. D. Mallet, and L. E. Brennan, "Rapid convergence rate in adaptive arrays", IEEE Transaction on Aerospace and Electronic Systems, vol. 53, no. 6, pp. 853-863, 1974.
  4. S. T. Smith, "Covariance, subspace and intrinsic Cramer-Rao bounds", IEEE Transaction on Signal Processing, vol. 53, no. 5, pp. 1610-1630, 2005. https://doi.org/10.1109/TSP.2005.845428
  5. J. F. Degurse, L. Savy, and S. Marcos, "On using a priori knowledge in space-time adaptive processing", IEEE Transaction on Signal Processing, vol. 56, no. 6, pp. 2598-2602, 2008. https://doi.org/10.1109/TSP.2007.914347
  6. G. Zhiqi, T. Haihong, "Robust STAP algorithm based on knowledge-aided SR for airborne radar", IET Radar, Sonar & Navigation, vol. 11, no. 2, pp. 321-329, 2017. https://doi.org/10.1049/iet-rsn.2016.0256
  7. K. L. Noh, "Space-time adaptive processing : A knowledge-based perspective for airborne radar", IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 51-65, 2006. https://doi.org/10.1109/MSP.2006.1593337
  8. J. R. Guerci, E. J. Baranoski, "Knowledge-aided adaptive radar at DARPA", IEEE Transaction on Signal Processing, vol. 23, no. 1, pp. 41-50, 2006. https://doi.org/10.1109/MSP.2006.1593336
  9. J. S. Bergin, C. M. Telxeira, P. M. Techau, and J. R. Guerci, "Improved clutter mitigation performance using knowledge-aided space-time adaptive processing", IEEE Transaction on Aerospace and Electronic Systems, vol. 42, no. 3, pp. 997-1009, 2006. https://doi.org/10.1109/TAES.2006.248194
  10. P. Stoica, J. Li, X. Zhu, and J. R. Guerci, "On using a priori knowledge in space-time adaptive processing", IEEE Transaction on Signal Processing, vol. 56, no. 6, pp. 2598-2602, 2008. https://doi.org/10.1109/TSP.2007.914347
  11. H. Jeon, Y. Chung, W. Chung, and H. Yang, "Clutter covariance matrix estimation using weight vectors in knowledge- aided STAP", IET Electronics Letters, vol. 53, Issue. 8, 2017.
  12. E. Aboutanios, B. Mulgrew, "Evaluation of the single and two data set STAP detection algorithms using measured data.", IGARSS 2007, Barcelona, pp. 23-28, 2007.
  13. M. S. Greco, S. Watts, Academic Press Library in Signal Processing: Volume 2 - Communications and Radar Signal Processing, chapter 12, Academic Press, New York, NY, 2014.
  14. M. S. Greco, S. Watts, Academic Press Library in Signal Processing: Volume 2 - Communications and Radar Signal Processing, Chapter 11, Academic Press, New York, NY, 2014.
  15. M. A. Richards, Fundamentals of Radar Signal Processing, McGraw-Hill-Interscience, 2005.