Spectrum Based Detector in Non-white Noise Environment

비백색 잡음 환경에 적합한 스펙트럼 기반 탐지기

  • Yu, Seog-Kun (School of Electrical Engineering and Computer Science, Kyungpook National University) ;
  • Joo, Eon-Kyeong (School of Electrical Engineering and Computer Science, Kyungpook National University)
  • 유석근 (경북대학교 전자전기컴퓨터학부) ;
  • 주언경 (경북대학교 전자전기컴퓨터학부)
  • Received : 2008.08.05
  • Published : 2009.10.25

Abstract

The MF(matched filter) is the optimum signal detector that maximizes the output instantaneous signal power to average noise power ratio in white noise environment. But it cannot give the optimum detection performance if the background noise is not white. So, the whitening process preceding the matched filter is needed in the conventional detector which results in a PWMF(pre-whitening matched filter). Its performance is mainly affected by the estimation accuracy of non-white noise model which is used in the whitening procedure. To estimate more accurate model to improve performance, the computational complexity is increased. Therefore, a spectrum based detector which shows better performance than the PWMF under the similar complexity condition or less complexity under the similar performance condition is proposed in this paper. And its performance and complexity are analyzed and compared with the conventional PWMF.

정합필터(matched filter)는 백색잡음 환경에서 출력 첨두 신호전력 대 평균 잡음전력의 비를 최대화하는 최적 탐지기이다. 하지만 비백색 잡음 환경에서는 정합필터를 이용하여 최적 탐지성능을 얻을 수 없다. 따라서 정합필터 이전에 백색화 과정을 수행하는 선백색화 정합필터(pre-whitening matched filter) 탐지기가 사용된다. 그리고 이것의 성능은 백색화 과정에 사용되는 비백색 잡음모델의 추정 정확성에 크게 영향을 받는다. 성능을 높이기 위해서는 정확한 잡음모델을 추정해야 하지만 이는 계산 복잡도를 많이 증가시킨다. 그러므로 본 논문에서는 기존 선백색화 정합필터 탐지기와 유사한 복잡도에서는 우수한 성능을 보이며, 유사한 성능에서는 낮은 복잡도를 보이는 스펙트럼 기반의 탐지기를 제안한다. 그리고 제안된 탐지기의 성능과 복잡도를 기존 탐지기와 비교하고 이를 분석한다.

Keywords

References

  1. B. Sklar, Digital communications: Fundamentals and applications, 2nd ed., Prentice-Hall Inc., Upper Saddle River, NJ, 2001
  2. V. Carmillet, P. O. Amblard, and G. Jourdain, 'Detection of phase- or frequency-modulated signals in reverberation noise,' J. Acoust. Soc. Amer., vol. 105, no. 6, pp. 3375-3389, June 1999 https://doi.org/10.1121/1.424666
  3. G. Ginolhac and G. Jourdain, 'Detection in presence of reverberation,' MTS/IEEE Conf. OCEANS 2000, Providence, RI, vol. 2, pp. 1043-1046, Sep. 2000 https://doi.org/10.1109/OCEANS.2000.881738
  4. S. L. Marple, Digital spectral analysis with applications, Prentice-Hall Inc., Englewood Cliffs, NJ, 1987
  5. S. Kay, Modern spectral estimation : Theory and application, Prentice-Hall Inc., Englewood Cliffs, NJ, 1988
  6. N. Lofgren, K. Lindercrantz, A. Flisberg, R. Bagenholm, I. Kjellmer, and M. Thordstein, 'Spectral distance for ARMA models applied to electroencephalogram for early detection of hypoxia,' J. Neural Eng., no. 3, pp. 277-234, July 2006
  7. V. Bykov, A. Kytmanov, M. Lazman, and M. Passare, Elimination method in polynomial computer algebra (Mathematics and its applications) , Kluwer Academic Publishers, Dordrecht, Netherlands, 1996
  8. A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-time signal processing, Prentice- Hall Inc., Upper Saddle River, NJ, 1999
  9. R. J. Urick, Principles of underwater sound, 3rd ed., McGraw-Hill, New York, 1988
  10. T. Collins, and P. Atkins, 'Doppler-sensitive active sonar pulse designs for reverberation processing,' Proc. IEE Radar, Sonar and Navigation, vol. 145, pp.347-353, Dec. 1998 https://doi.org/10.1049/ip-rsn:19982434
  11. S.-K. Yu, Spectrum based detector for active sonar in reverberation environment, M.S. Thesis, Kyungpook National University, Feb. 2007