Browse > Article
http://dx.doi.org/10.5573/ieie.2014.51.4.181

SIR analysis for Enhancing Image Quality in Underwater Acoustic Lens System  

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)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.4, 2014 , pp. 181-190 More about this Journal
Abstract
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.
Keywords
SIR; acoustic lens; sonar; beam pattern; apodization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Dundgeon, Dan E. and Don H. Johnson, Array Signal Processing, Concepts and Techniques PRT Prentice Hall, Englewood Cliffs, NJ, 1993. Chapter 3.
2 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
3 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
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   DOI
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 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