DOI QR코드

DOI QR Code

수중음향렌즈 카메라에서 영상 품질 향상을 위한 SIR 분석

SIR analysis for Enhancing Image Quality in Underwater Acoustic Lens System

  • 이지은 (숭실대학교 정보통신공학과) ;
  • 임성빈 (숭실대학교 정보통신공학과) ;
  • 심태보 (숭실대학교 정보통신공학과)
  • Lee, Jieun (Department of Information & Telecommunication Engineering, Soongsil University) ;
  • Im, Sungbin (Department of Information & Telecommunication Engineering, Soongsil University) ;
  • Shim, Taebo (Department of Information & Telecommunication Engineering, Soongsil University)
  • 투고 : 2013.12.09
  • 심사 : 2014.04.03
  • 발행 : 2014.04.25

초록

수중음향렌즈 시스템은 해저를 탐사하여 고해상도의 영상을 얻어내는 시스템의 하나로 음향렌즈를 이용하여 빔을 형성한다. 음향렌즈를 이용하여 빔을 형성함으로써 복잡도를 낮추고 구동전력을 절감시킨다. 음향렌즈로부터 들어오는 빔을 배열센서로 수신하려면 음향렌즈의 빔패턴을 분석하여 센서간격을 설정하는 문제를 해결해야 한다. 여기서 SIR (Signal to Interference Ratio)을 사용하여 센서간격과 빔패턴과 영상 품질과의 관계를 분석한다. 빔패턴의 모양에 따라 SIR은 변하므로 대체로 센서간격이 넓으면 사이드로브의 영향이 감소하므로 SIR이 개선된다. 사이드로브의 크기가 크면 전반적으로 SIR이 악화된다. Apodization 함수에 따라 빔패턴의 메인로브의 폭과 모양과 사이드로브의 모양과 준위가 바뀐다. 따라서 적절한 apodization 함수를 적용하여 SIR을 개선한다. 본 논문에서 예시된 빔패턴의 경우 센서간격이 13mm에서 SIR이 0-10dB로 안정이 되지만, 그 값이 높지 않다. 따라서 Chebyshev 함수를 적용하여 37mm 이상의 구간에서 80dB의 SIR을 얻는다. 센서간격이 더 작은 경우는 Hann 함수나 triangular 함수가 좋은 성능을 나타냈다.

The underwater acoustic lens system is one of the systems getting high-resolution images on the seafloor by the beam forming method using acoustic lens. The beam forming using acoustic lenses reduces complexity and driving power. When receiving an incoming beam with the acoustic lens array, beam pattern analysis and arrangement problem of the array sensor must be addressed. Introducing SIR (Signal to Interference Ratio), the relationship among sensor interval, beam pattern and image quality would be analyzed. Generally if the sensor interval getting wider, the less effect of the side lobes makes SIR high. If the amplitude of a side lobe is high, SIR is generally getting low. The type of the apodization function changes the width, shape and amplitude of both main lobe and side lobes. Thus an appropriate apodization function can improve SIR. In this paper, SIR is stable at the sensor interval of 13mm with 0-10dB, which is not high relatively. By applying the Chebyshev function, the SIR becomes 80dB over the sensor interval of 37 mm or higher. The Hann and triangular functions demonstrate better SIR when the sensor interval becomes narrower.

키워드

참고문헌

  1. Edward O. Belcher, Dama C. Lynn, Hien Q. Dinh, Thomas J. Laughlin,"Beamforming and Imaging with Acoustic Lenses in Small, High-frequency Sonars", The Proceedings of Oceans '99 Conference, vol.3, pp. 1495-1499, 1999, Seattle WA
  2. Edward O. Belcher, CWO2 Jeffery R. Barone, Dennnis G. Gallagher, Robald E. Honaker, "Acoustic Lens Camera and Underwater Display Combine to Provide Efficient and Effective Hull and Berth Inspections", Oceans Conference 2003, vol. 3, pp. 1361-1367, 2003, San Diego, CA
  3. Edward O. Belcher, Brian Matsuyama, Gary Trimble, "Object Identification with Acoustic Lenses", OCEANS, 2001. MTS/IEEE Conference and Exhibition, vol.1, pp.6-11, 2001, Honolulu, HI
  4. Kevin Fink, "Computer Simulation of Pressure Fields Generated by Acoustic Lens Beamformers", M.S. Thesis. University of washington, 1994
  5. Anthony J.Bladek, "Visualization of Acoustic Lens Data", Visualization '93, Proceedings., IEEE Conference on, pp316 - 323, San Jose, CA, 1993
  6. Y. Takase T. Anada T. Tsuchiya N. Endoh N.Nakamwa T. Tukioka, "Real-Time Sonar System using Acoustic Lens and Numerical Analysis based on 2D/3D Parabolic Equation method", Japanese Journal of Applied Physics. vol 41, pp. 3509-3512, 2002 https://doi.org/10.1143/JJAP.41.3509
  7. Ju Wu, Hongyu Bian, "Beam-forming and Imaging Using Acoustic Lenses: Some Simulation and Experimental Result", Signal Processing Systems (ICSPS), 2010 2nd International Conference on, vol. 2, pp. V2-764- V2-768, 2010, Dalian
  8. Matthias Woelfel, John McDonough, Distant Speech Recognition, Wiley, 2009, Chap 13. Beamforming
  9. Peter Lynch.,"The Dolph-Chebyshev Window: A Simple Optimal Filter", Monthly weather Review, Vol 125, pp. 655-660, 1996
  10. Oppenheim, A.V., R.W. Schafer, Discrete-Time Signal Processing, Prentice-Hall, 1989, pp. 447-448
  11. Oppenheim, A.V., R.W. Schafer, Discrete-Time Signal Processing, Prentice-Hall, 1989, pp. 453
  12. Sophocles J. Orfanidis, Electromagnetic Waves and Anttennas, 2002, Chapter 17.
  13. Emilson Pereira Leite, Matlab - Modelling, Programming and Simulations, Sciyo, 2010, Chapter 13.
  14. Dundgeon, Dan E. and Don H. Johnson, Array Signal Processing, Concepts and Techniques PRT Prentice Hall, Englewood Cliffs, NJ, 1993. Chapter 3.