The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of statistical characteristics of bistatic reverberation in the east sea

동해 해역에서 양상태 잔향음 통계적 특징 분석

  • 염수현 (서울대학교 조선해양공학과) ;
  • 윤승현 (서울대학교 조선해양공학과) ;
  • 양해상 (서울대학교 조선해양공학과) ;
  • 성우제 (서울대학교 조선해양공학과)
  • Received : 2022.05.23
  • Accepted : 2022.07.04
  • Published : 2022.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, the reverberation of a bistatic sonar operated in southeastern coast in the East Sea in July 2020 was analyzed. The reverberation sensor data were collected through an LFM sound source towed by a research vessel and a horizontal line array receiver 1 km to 5 km away from it. The reverberation sensor data was analyzed by various methods including geo-plot after signal processing. Through this, it was confirmed that the angle reflected from the sound source through the scatterer to the receiver has a dominant influence on the distribution of the reverberation sound, and the probability distribution characteristics of bistatic sonar reverberation varies for each beam. In addition, parametric factors of K distribution and Rayleigh distribution were estimated from the sample through moment method estimation. Using the Kolmogorov-Smirnov test at the confidence level of 0.05, the distribution probability of the data was analyzed. As a result, it could be observed that the reverberation follows a Rayleigh probability distribution, and it could be estimated that this was the effect of a low reverberation to noise ratio.

최근 단상태 소나의 표적탐지에 대한 한계로 인해 양상태 소나의 사용이 늘어나고 있다. 또한 소나 시스템이 고해상도의 성능으로 발전하면서 잔향음의 확률분포가 다양한 형태로 나타나 이에 대한 연구가 활발히 이루어 지고 있다. 본 연구에서는 2020년 7월, 동해 천해환경에서 수행된 양상태 소나의 잔향음을 분석하였다. 잔향음 센서 데이터는 Linear Frequency Modulated(LFM) 음원을 예인하는 연구선과 이로부터 1 km~ 5 km 떨어진 수평 선배열 수신기를 통해 수집되었으며 빔 형성 및 프리엠프 게인(Preamp-gain) 보상과정 등의 신호처리를 거친 후 Geo-plot을 비롯한 다양한 방법으로 분석되었다. 이를 통해 음원에서 산란체를 거쳐 수신기로 반향되는 각도가 잔향음의 분포에 지배적인 영향을 미친다는 점과 빔별로 잔향음 확률분포가 달라진다는 양상태 소나 잔향음의 특징을 확인할 수 있었다. 또한, 모멘트 추정 기법을 통해 샘플로부터 K 분포 및 레일리 분포의 모수 인자를 추정하였으며 Kolmogorov-Smirnov test(K-S test) 기법을 이용하여 유의수준 0.05에서 데이터가 어느 확률분포에 일치하는 지를 분석하였다. 결과적으로 잔향음이 레일리 확률분포를 따른다는 것을 관찰할 수 있었으며 이는 낮은 Reveration to Noise Ratio(RNR)의 영향임을 추정할 수 있었다.

Keywords

Acknowledgement

본 연구는 국방과학연구소(ADD)의 지원을 받아 수행 되었습니다(계약번호: UD200005DD).

References

  1. G. L. Liang, Y. Zhang, G. P. Zhang, and K. Liu, "Research on the frequency hopping bistatic sonar system," Proc. ICGIP, 8285 (2011).
  2. J. Yang, D. Tang, B. T. Hefner, K. L. Williams, and J. R. Preston, "Overview of midfrequency reverberation data acquired during the target and reverberation experiment 2013," IEEE J. Oceanic Eng. 43, 563-585 (2018). https://doi.org/10.1109/joe.2018.2802578
  3. J. R. Preston and W. A. Kinney, "Using triplet arrays forbroadband reverberation analysis and inversions," IEEE J. Ocean. Eng. 32, 879-896 (2007). https://doi.org/10.1109/JOE.2007.899279
  4. D. D. Ellis, J. Yang, J. R. Preston, and S. Pecknold, "A normal mode reverberation and target echo model to interpret towed array data in the target and reverberation experiments," IEEE J. Oceanic Eng. 42, 344-361 (2017). https://doi.org/10.1109/JOE.2017.2674106
  5. J. R. Preston and D. D. Ellis, "Extracting bottom information from towed-array reverberation data Part I: Measurement methodology," J. Marine Systems. 78, S359-S371 (2009). https://doi.org/10.1016/j.jmarsys.2009.01.034
  6. D. D. Ellis and J. R. Preston, "Extracting bottom information from towed-array reverberation data Part II: Extraction procedure and modeling methodology," J. Marine Systems, 78, S372-S381 (2009). https://doi.org/10.1016/j.jmarsys.2009.01.035
  7. D. Middleton, "New physical-statistical methods and models for clutter and reverberation: The KA-distribution and related probability structures," IEEE J. Oceanic Eng. 24, 261-284 (1999). https://doi.org/10.1109/48.775289
  8. J. R. Preston and D. A. Abraham "Statistical analysis of multistatic echoes from a shipwreck in the malta plateau," IEEE J. Oceanic Eng. 40, 643-656 (2015). https://doi.org/10.1109/JOE.2014.2331533
  9. A. P. Lyons and D. A. Abraham, "Statistical characterization of high frequency shallow-water sea-floor back-scatter," J. Acoust. Soc. Am. 106, 1307-1315 (1999). https://doi.org/10.1121/1.428034
  10. D. A. Abraham and A. P. Lyons, "Novel physical interpretations of K-distributed reverberation," IEEE J. Oceanic Eng. 27, 800-813 (2002). https://doi.org/10.1109/JOE.2002.804324
  11. D. A. Abraham and A. P. Lyons, "Simulating non-Rayleigh reverberation and clutter," IEEE J. Oceanic Eng. 29, 347-362 (2004). https://doi.org/10.1109/JOE.2004.828202
  12. J. R. Preston and D. A. Abraham, "Non-Rayleigh reverberation characteristics near 400 Hz observed on the New Jersey shelf," IEEE J. Oceanic Eng. 29, 126-137 (2004). https://doi.org/10.1109/JOE.2004.824039
  13. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C : The Art of Scientific Computing (Cambridge University Press, New York, 1992), Chapter 14.
  14. Y. C. Jung, K. H. Lee, W. J. Seong, and S. G. Kim, "Ocean bottom reverberation and its statistical characteristics in the East Sea"(in korean), J. Acoust. Soc. Kr. 38, 82-95 (2019).
  15. C. H. Harrison, "SUPREMO : A multistatic sonar performance model," NATO SACLANT Undersea Research Centre. SM-396, Rep., 2002.
  16. M. Fisz, Probability Theory and Mathematical Statistics (John Wiley & Sons, New York, 1963), pp. 394-449.
  17. S. Watts, "Radar detection prediction in K-distributed sea clutter and thermal noise," IEEE Trans. Audio Eng. Soc. AE-23, 40-45 (1987).