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A new form of Gabor Wigner Transform by adaptive thresholding in Gabor Transform and Wigner Distribution and the power of signal synthesis techniques to enhance the strengths of GWT

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Języki publikacji
EN
Abstrakty
EN
In this paper, a modified form of the Gabor Wigner Transform (GWT) has been proposed. It is based on adaptive thresholding in the Gabor Transform (GT) and Wigner Distribution (WD). The modified GWT combines the advantages of both GT and WD and proves itself as a powerful tool for analyzing multi-component signals. Performance analyses of the proposed distribution are tested on the examples, show high resolution and crossterms suppression. To exploit the strengths of GWT, the signal synthesis technique is used to extract amplitude varying auto-components of a multi-component signal. The proposed technique improves the readability of GWT and proves advantages of combined effects of these signal processing techniques.
Rocznik
Strony
99--106
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
autor
autor
autor
  • Muhammad Ali Jinnah University, Department of Electronic Engineering, Kahuta Road, Zone-V, Islamabad, Pakistan, ajabatcop@yahoo.com
Bibliografia
  • [1] Cohen, L. (1989). Time-frequency distributions-A review. In Proc. IEEE, 77, 94-981.
  • [2] Hlawatch, F., Boudreaux-Bartels, G.F. (1992). Linear and quadratic time frequency signal representations. IEEE Signal Processing Magazine, 9(4), 21-67.
  • [3] Shafi, I., Ahmad, J., Shah, S.I., Kashif, F.M. (2009). Techniques to Obtaian Good Resolution and Concentrated Time-Frequency Distributions: A Review. EURASIP Journel on Advances in Signal processing, 2009, art. ID 673539, DOI: 10.1155/2009/673539.
  • [4] Boashash, B. (2003). Time-Frequency Signal Analysis and Processing. Prentice-Hall, Upper Saddle River, NJ, USA.
  • [5] Qazi, S., Georgakis, A., Stergioulas, L.K., Bahaei, M.S. (2007). Interference suppression in the Wigner distribution using fractional Fourier transformation and signal synthesis. IEEE Trans. Signal Process., 55, 3150-3154.
  • [6] Khan, N.A., Taj, I.A., Jaffri, N., Ijaz, S. (2011). Cross-term elimination in Wigner distribution based on 2D signal processing techniques. Signal Proc., advances in Fractional Signals and Systems, 91(3), 590-599.
  • [7] Ajab, M., Taj, A.I. , Khan, A.N. (2012). COMPARATIVE ANALYSIS OF VARIANTS OF GWT FOR CROSS-TERMS REDUCTION. Metrol. Meas. Syst., 19(3), 499-508.
  • [8] Pei, S.C., Ding, J.J. (2007). Relations between Gabor transforms and fractional Fourier transforms and their applications for signal processing. IEEE Trans. Signal Processing, 55(10), 4839-4850.
  • [9] Szmajda, M., Górecki, K., Mroczka, J. (2010). Gabor Transform, SPWVD, Gabor-Wigner Transform and Wavelet Transform - Tools for Power Quality monitoring. Metrol. Meas. Syst., 17(3), 383-396.
  • [10] Acharya, T., Ray, A. (2005). Image Processing: Principles and Applications. Wiley Interscience, Hoboken, NJ.
  • [11] Boudreaux-Bartels, F.G., Parks, T. (1986). Time-varying filtering and signal estimation using Wigner distribution synthesis techniques. IEEE Trans. Acoust., Speech, Signal Process., ASSP-34(3), 442-451.
  • [12] Hassnpour, H. (2008). A time-frequency approach for noise reduction. Digital Signal Processing, 18, 728-738.
  • [13] Jones, D., Park, T. (1992). A resolution comparison of several time-frequency representations. IEEE Trans. Signal Process., 40, 413-420.
  • [14] Williams, W.J., Brown, M., Hero, A. (1991). Uncertainty, information and time frequency distributions. In SPIE, Advanced Signal Processing Algorithms, 1556, 144-156.
  • [15] Shannon, C. E. (1948). A mathematical theory of communication. Part I, Bell Sys. Tech J., 27, 379-423.
  • [16] Stankovic, Jubisa. (2001). A Measure of Some Time-Frequency Distributions concentration. Signal Processing, 81(3), 212-223.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0113-0009
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