YIN Baiqiang, HE Yigang, LI Bing, ZUO Lei, YUAN Lifen. An Adaptive SVD Method for Solving the Pass-Region Problem in S-Transform Time-Frequency Filters[J]. Chinese Journal of Electronics, 2015, 24(1): 115-123.
Citation: YIN Baiqiang, HE Yigang, LI Bing, ZUO Lei, YUAN Lifen. An Adaptive SVD Method for Solving the Pass-Region Problem in S-Transform Time-Frequency Filters[J]. Chinese Journal of Electronics, 2015, 24(1): 115-123.

An Adaptive SVD Method for Solving the Pass-Region Problem in S-Transform Time-Frequency Filters

Funds:  This work is supported by the National Natural Science Foundation for Distinguished Young Scholars of China (No.50925727), the Young Scientists Fund of the National Natural Science Foundation of China (No.51107034, No.61102035), the National Defense Advanced Research Project (No.C1120110004, No.9140A27020211DZ5102), Foundation for Key Program of Ministry of Education, China (No.313018),the Natural Science Foundation of Hunan Province, China (No.12JJA004, No.2011J4, No.2011JK2023) and the Research Foundation of Education Bureau of Hunan Province, China (No.11C0479).
  • Received Date: 2013-11-01
  • Rev Recd Date: 2014-04-01
  • Publish Date: 2015-01-10
  • S-transform (ST) is an excellent tool for time-frequency filter. There are two factors that influence filtering performance: Inverse s-transform (IST) algorithms and the pass-regions in time-frequency domain. A novel matrix IST algorithm is derived and an adaptive Singular value decomposition (SVD) method for solving the pass-region problem is proposed. The former can avoid reconstructing errors in time-frequency filtering; the latter is effective to distinguish the pass-region of signal from noise. Filter can be realized by removing the smaller singular values and keeping the larger singular values. An additive noise perturbation model is built in ST time-frequency domain and the effective rank of noise perturbation model based on matrix IST is analyzed. Simulation results indicate that the proposed SVD method can provide higher precision than the existing ones at low signal-to-noise ratio and does not need to compute the noise statistics property. Illustrative examples verify the effectiveness of proposed method.
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  • R.A. Brown, M.L. Lauzon and R.A. Frayne, "General description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous Stransform spectrum nonredundantly", IEEE Trans. Signal Process., Vol.58, No.1, pp.281-290, 2010.
    C. Simon, S. Ventosa, M. Schimmel et al., "The S-transform and its inverse: Side effects of discretizing and filtering", IEEE Trans. Signal Process., Vol.55, No.10, pp.4928-4937, 2007.
    M. Biswal and P.K. Dash, "Estimation of time-varying power quality indices with an adaptive window-based fast generalised S-transform", IET Science Measurement & Technology, Vol.6, No.4, pp.189-197, 2012.
    Z. Li, F. Ji, Z.G. Jie, et al., "Wavelet transformation for magnetocardiography signal", Chinese Physics B, Vol.54, No.4, pp.1943-1949, 2005.
    R. Pinnegar, H. Khosravani and P. Federico, "Time-frequency phase analysis of ictal EEG recording with the S-transform", IEEE Trans. Signal Process., Vol.56, No.11, pp.2583-2593, 2009.
    S. Ventosa, C. Simon, M. Schimmel, et al., "The S-transform from a wavelet point of view", IEEE Trans. Signal Process., Vol.56, No.7, pp.2771-2780, 2008.
    R.G. Stockwell, L. Mansinha and R.P. Lowe, "Localization of the complex spectrum: The S-transform", IEEE Trans. Signal Process.,Vol.44, No.4, pp.998-1001, 1996.
    K.R. Krishnanand and P.K. Dash, "A new real-time fast discrete S-transform for cross-differential protection of shuntcompensated power systems", IEEE Transactions on Power Delivery, Vol.28, No.1, pp.402-410, 2013.
    M. Schimmel and J. Gallart, "Author’s reply to comments on ‘The inverse S-transform in filters with time-frequency localization’ ", IEEE Trans. Signal Process., Vol.55, No.10, pp.5120- 5121, 2007.
    M. Schimmel and J. Gallart, "The inverse S-transform in filters with time-frequency localization", IEEE Trans. Signal Process., Vol.53, No.11, pp.4417-4422, 2005.
    C.R. Pinnegar, "Comments on ‘The inverse S-transform in filters with time-frequency localization’ ", IEEE Trans. Signal Process., Vol.55, No.10, pp.5117-5120, 2007.
    S.C. Pei and P.W.Wang, "Novel inverse S-transform with equalization filter", IEEE Trans. Signal Process., Vol.57, No.10, pp.3858-3868, 2009.
    W.S. Man and J.H. Gao, "Statistical denoising of signals in the S-transform domain", Computers & Geosciences., Vol.35, No.6, pp.1079-1086, 2009.
    C.H. Yu and R.S.C. Cobbold, "Single ensemble-based eigenprocessing methods for color flow imaging-Part I. The Hankel- SVD filter", IEEE Trans. Ultrasonics Ferroelectrics and Frequency Control, Vol.55, No.3, pp.559-572, 2008.
    J. Wang, P. Lei, J.P. Sun, et al., "Micro-Doppler signal analysis based on the generalized S transform for radar targets", Chinese Journal of Electronics, Vol.19, No.4, pp.769-774, 2010.
    W. Zhang, R. Tao and Y. Wang, "Linear canonical S transform", Chinese Journal of Electronics, Vol.20, No.1, pp.63-66, 2011.
    B.Q. Yin, Y.G. He and X.M. Wu, "A method for magnetocardiograms filtering based on singular value decomposition and S-transform", Acta Phys. Sin., Vol.62, No.14, pp.148702-1- 148702-8, 2013. (in Chinese)
    S.F. He, K.C. Li and M. Zhang, "A real-time power quality disturbances classification using hybrid method based on Stransform and dynamics", IEEE Transactions on Instrumentation and Measurement, Vol.62, No.9, pp.2465-2475, 2013.
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