Back
 JCC  Vol.2 No.2 , January 2014
Characteristic Analysis of White Gaussian Noise in S-Transformation Domain
Abstract: The characteristic property of white Gaussian noise (WGN) is derived in S-transformation domain. The results show that the distribution of normalized S-spectrum of WGN follows X2 distribution with two degrees of freedom. The conclusion has been confirmed through both theoretical derivations and numerical simulations. Combined with different criteria, an effective signal detection in S-transformation can be realized.
Cite this paper: Zhang, X. , Qi, Y. and Zhu, M. (2014) Characteristic Analysis of White Gaussian Noise in S-Transformation Domain. Journal of Computer and Communications, 2, 20-24. doi: 10.4236/jcc.2014.22004.
References

[1]   R. G. Stockwell, L. Mansinha and R. P. Lowe, “Localization of the Complex Spectrum: The S-Transform,” IEEE Transactions on Signal Processing, Vol. 44, No. 4, 1996, pp. 998-1001. http://dx.doi.org/10.1109/78.492555

[2]   C. R. Pinnegar and L. Mansinha, “The S-Transform with Windows of Arbitrary and Varying Shape,” Geophysics, Vol. 68, No. 1, 2003, pp. 381-385. http://dx.doi.org/10.1190/1.1543223

[3]   A. G. Rehorn, E. Sejdic and J. Jiang, “Fault Diagnosis in Machine Tools Using Selective Regional Correlation,” Mechanical Systems and Signal Processing, Vol. 20, No. 5, 2006, pp. 1221-1238. http://dx.doi.org/10.1016/j.ymssp.2005.01.010

[4]   G. Livanos, N. Ranganathan and J. Jiang, “Heart Sound Analysis Using the S-Transform,” Proceedings of Computers in Cardiology, Cambridge, 24-27 September 2000, 587-590.

[5]   C. R. Pinnegar and L. Mansinha, “The Bi-Gaussian S-Transform,” SIAM: SIAM Journal on Scientific Computing, Vol. 24, No. 5, 2003, pp. 1678-1692. http://dx.doi.org/10.1137/S1064827500369803

[6]   C.R. Pinnegar, L. Mansinha, “Time-Local Fourier Analysis with a Scalable, Phase-Modulated Analyzing Function: The S-Transform with a Complex Window,” Signal Process, Vol. 84, No. 7, 2004, pp. 1167-1176. http://dx.doi.org/10.1016/j.sigpro.2004.03.015

[7]   M. Schimmel and J. Gallart, “The Inverse S-Transform in Filters with Time-Frequency Localization,” IEEE Transactions on Signal Process, Vol. 53, No. 11, 2005, pp. 4417-4422. http://dx.doi.org/10.1109/TSP.2005.857065

[8]   C. Si-mon, S. Ventosa, M. Schimmel, A. Heldring, J. J. Da?obeitia, J. Gallart and A. Mànuel, “The S-Transform and Its Inverses: Side Effects of Discretizing and Filtering,” IEEE Transactions on Signal Processing, Vol. 55, No. 10, 2007, pp. 4928-4937. http://dx.doi.org/10.1109/TSP.2007.897893

[9]   E. Sejdic, L. Stankovic, M. Dakovic and J. Jiang, “Instantaneous Frequency Estimation Using the S-Transform,” IEEE Signal Processing Letters, Vol. 15, 2008, pp. 309-312. http://dx.doi.org/10.1109/LSP.2008.917014

[10]   E. Sejdic and J. Jiang, “Selective Regional Correlation for Pattern Recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37, No. 1, 2007, pp. 82-93.

[11]   J. H. Gao, W. S. Man and S. M. Chen, “Recognition of Signals from Colored Noise Background in Generalized S-Transformation Domain,” Chinese Journal of Geophysics, Vol. 47, No. 5, 2004, pp. 869-875. http://dx.doi.org/10.1002/cjg2.576

 
 
Top