Underwater Transient Signal Detection Using Higher-order Statistics and Wavelet Analysis

고차통계 기법과 웨이브렛을 이용한 수중 천이신호 탐지

  • 조환래 (한양대학교 지구해양과학과 해양음향연구실) ;
  • 오선택 (한양대학교 지구해양과학과 해양음향연구실) ;
  • 오택환 (한양대학교 지구해양과학과 해양음향연구실) ;
  • 나정열 (한양대학교 지구해양과학과 해양음향연구실)
  • Published : 2003.11.01

Abstract

This paper deals with application of wavelet transform, which is known to be good for time-frequency analysis, in order to detect the underwater transient signals embedded in ambient noise. A new detector of acoustic transient signals is presented. It combines two detection tools: wavelet analysis and higher-order statistics. Using both techniques, the detection of the transient signal is possible in low signal to noise ratio condition. The proposed algorithm uses the wavelet transform of a partition of the signal on frequency domain, and then higher-order statistics tests the Gaussian nature of the segments.

본 논문에서는 수중 천이신호 탐지를 위하여 시간주파수 영역에서 신호분석이 가능한 웨이브렛을 적용하였다. 낮은 신호대 잡음비를 가지는 관측신호로부터 천이신호를 탐지하기 위하여 고차통계 기법과 웨이브렛을 사용하였으며, 웨이브렛을 이용하여 신호를 주파수 영역에서 분해한 다음 고차통계 기법을 이용하여 분해된 웨이브렛 계수들의 정규분포 특성을 측정하였다. 제안한 방법으로 천이신호를 탐지할 경우 낮은 신호대 잡음비를 가지는 관측 신호로부터 천이신호를 잘 탐지할 수 있었다.

Keywords

References

  1. P. Ravier and P. O. Amblard, 'Combining an adapted wavelet analysis with fourth-order statistics for transient detection,' Signal Processing, 70, 115-128, 1998 https://doi.org/10.1016/S0165-1684(98)00117-0
  2. P. Ravier and P. O. Amblard, 'Wavelet packets and denoising based on higher-order-statistics for transient detection,' Signal Processing, 81, 1909-1926, 2001 https://doi.org/10.1016/S0165-1684(01)00088-3
  3. R. J. Urick, Principles of Underwater Sound, 3^r^d ed., McGrawHill, New York, Chap. 7, 202-236, 1993
  4. 강현배, 김대경, 서진근, Wavelet Theory and Its Applications, 대우학술총서509, 아카넷, 제4장, 67-79, 2001
  5. S. G. Mallat, 'A theory for multiresolution signal decomposition: The wavelet presentation,' IEEE Trans. Pattern Anal. Machine Intell., 11, 674-693, 1989 https://doi.org/10.1109/34.192463
  6. R. Coitman and M. Wickerhauser, 'Entropy based algorithms for best basis selection,' IEEE trans. Inform. Theory, 38, 713-718, 1992 https://doi.org/10.1109/18.119732
  7. R. Coilman, Y. Meyer, and M. Wickerhauser, 'Wavelet Analysis and Signal Processing,' in Wavelet and Their Applications, B. Ruskai, et al. eds., Jones and Barlett Pub., Boston, 153-178, 1992
  8. H. J. Larson, Introduction to Probability, Addison-Wesley Pub. Co, Chap. 2, 102-117, 1995
  9. M. G. Kendall and, A. Stuart, The Advanced Theory of Statistics Distribution Theory, Charles Griffin & Company Limited, London & High Wycombe, Chap. 2, 243-262, 1977
  10. I. Daubechies, 'The wavelet transform time, frequency localization and signal analysis,' IEEE Trans. Inform. Theory, 36, 961-1005, 1990 https://doi.org/10.1109/18.57199
  11. R. J. Barsanti, Jr., Denoising of Ocean Acoustic Signals Using Wavelet-based Techniques, Master Thesis, Naval Postgraduate School, USA, Chap. 7, 90-91, 1996