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Reseach Article

Beamforming Using Wavelet Transform

Published on None 2011 by Jeena Joy, Dr J Kanakaraj
International Conference on VLSI, Communication & Instrumentation
Foundation of Computer Science USA
ICVCI - Number 13
None 2011
Authors: Jeena Joy, Dr J Kanakaraj

Jeena Joy, Dr J Kanakaraj . Beamforming Using Wavelet Transform. International Conference on VLSI, Communication & Instrumentation. ICVCI, 13 (None 2011), 25-30.

author = { Jeena Joy, Dr J Kanakaraj },
title = { Beamforming Using Wavelet Transform },
journal = { International Conference on VLSI, Communication & Instrumentation },
issue_date = { None 2011 },
volume = { ICVCI },
number = { 13 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 25-30 },
numpages = 6,
url = { /proceedings/icvci/number13/2727-1507/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 International Conference on VLSI, Communication & Instrumentation
%A Jeena Joy
%A Dr J Kanakaraj
%T Beamforming Using Wavelet Transform
%J International Conference on VLSI, Communication & Instrumentation
%@ 0975-8887
%N 13
%P 25-30
%D 2011
%I International Journal of Computer Applications

This paper, aims to analyse, time domain beamforming on wavelet decomposed signals. Using a multiresolution signal representation extra computational effort is required by the wavelet processing. But due to reduced dimensionality of the subband data, the computational load for beamforming is reduced significantly. Advantage of using discrete wavelet transform is that it is capable of providing both time and frequency information simultaneously, which gives a joint time-frequency representation of the signal. Also it is apt for both stationary and non stationary signals and is the most appropriate system in the field of signal detection. Discrete wavelet transform is implemented through multi resolution analysis and digital filter banks. Wavelet analysis is the breaking up of a signal into a set of scaled and translated versions of an original (or mother) wavelet. In wavelet transform, original signal is decomposed into wavelet coefficients. These coefficients represent the signal in the wavelet domain and all data operations can be performed using just the corresponding wavelet coefficients. The major issue concerning wavelet processing is choosing optimal wavelet for signals. The performance of time domain beamformer is analyzed using different wavelets and db9 is found to be optimum.

  1. Agostiino Abbate, Casinemer M.DeCusatis , Pankaj K Das, “Wavelets and Sub bands”, Birkhauser, Boston, 2002
  2. Raghuveer M. Rao & Ajit S. Bopadiker , “Wavelet Transforms –Introduction to Theory and Applications", Pearson Education Asia, 1998.
  3. K.P. Soman & K. I. Ramachandran “Insight Into Wavelets - From Theory to Practice”, Prentice Hall, India, 2004
  4. Shie Qian & Dapang Chen , “Joint Time - Frequency Analysis -Methods and Applications”, Prentice Hall PTR, New Jersey,1996
  5. Richard O. Nieslsen , “Sonar Signal Processing” , Artech House Inc. Norwood , USA ,1991
  6. Petre Stocia & Randolph L Moses, “Introduction to Spectral Analysis” , Prentice Hall, New Jersey , USA ,1997
  7. Wen Xu, W. Kenneth Stewart, “Multiresolution–Signal Direction-of-Arrival Estimation: A Wavelets Approach”, IEEE Transactions on Signal Processing, vol. 7 , pp. 66 -68, March2000
  8. Martin Vetterli, Cormac Herley, “Wavelets and Filter Banks: Theory and Design”, IEEE Transactions on Signal Processing, vol. 40, pp. 2207-2231, Sep1992.
  9. Amara Graps, “An Introduction to Wavelets”, IEEE Computational Science and Engineering, pp.50-61, Summer1995
  10. Stephane G. Mallat, “A Theory for Multi Resolution Signal Decomposition: The Wavelet Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11 , pp. 674-693, July1985.
  11. Wen Xu, Te-Chich Liu, Henrik Schmidt, “Beamforming Based On Spatial-Wavelet Decomposition”, IEEE Journal of Oceanic Engineering, vol. 17 , pp. 480 - 484, April2002
  12. S Mallat, W.L.Hwang, “Singularity Detection and Processing with Wavelets”, IEEE Transactions on Information Theory, vol. 38, pp.617-643, March1992.
  13. B.D. Van Veen and K. M. Buckley, “Beamforming: A Versatile Approach to Spatial Filtering” , IEEE ASSP Magazine, pp. 4–24, Apr. 1988
  14. Gregory M. Kautz, Micheal D. Zollowski “Beamspace DOA Estimation Featuring Multirate Eigenvector Processing ”, IEEE Transactions on Signal Processing, vol. 44 , pp. 1965 - 1978, July 1996
  15. M Misiti, Y Misisiti, G Oppenheim and P. Poggi, “Matlab Wavelet Tool Box”, Math Works Inc., 2000.
Index Terms

Computer Science
Information Sciences


Beamforming wavelet transform discrete wavelet transform multiresolution analysis