Robust time-frequency representation based on the signal normalization and concentration measures

An efficient procedure for obtaining time-frequency representations under high influence of impulsive noise is proposed in this paper. The procedure uses the fast Fourier transform based algorithm instead of sorting procedures common in the case of various robust time-frequency representations proposed recently. Concentration measure is used to select a free parameter of the transform.

[1]  Ljubisa Stankovic,et al.  A method for time-frequency analysis , 1994, IEEE Trans. Signal Process..

[2]  K. V. Arya,et al.  Image registration using robust M-estimators , 2007, Pattern Recognit. Lett..

[3]  Ioannis Pitas,et al.  Digital Image Processing Algorithms and Applications , 2000 .

[4]  Vladimir Katkovnik Robust M-periodogram , 1998, IEEE Trans. Signal Process..

[5]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[6]  Gonzalo R. Arce,et al.  Robust time-frequency representations for signals in /spl alpha/-stable noise using fractional lower-order statistics , 1997, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics.

[7]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[8]  LJubisa Stankovic,et al.  Robust Wigner distribution with application to the instantaneous frequency estimation , 2001, IEEE Trans. Signal Process..

[9]  Vladimir V. Lukin,et al.  An Overview of the Adaptive Robust DFT , 2010, EURASIP J. Adv. Signal Process..

[10]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[11]  Ljubisa Stankovic,et al.  Highly concentrated time-frequency distributions: pseudo quantum signal representation , 1997, IEEE Trans. Signal Process..

[12]  Vladimir V. Lukin,et al.  Combination of non-linear filters in time and frequency domain , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

[13]  Igor Djurovic,et al.  Robust time-frequency distributions , 2001, Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467).

[14]  LJubisa Stankovic,et al.  Instantaneous frequency estimation using higher order L-Wigner distributions with data-driven order and window length , 2000, IEEE Trans. Inf. Theory.

[15]  Philippe Forster,et al.  Robust covariance matrix estimates with attractive asymptotic properties , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[16]  LJubisa Stankovic,et al.  A measure of some time-frequency distributions concentration , 2001, Signal Process..

[17]  Ling Shao,et al.  Robust Processing of Nonstationary Signals , 2010, EURASIP J. Adv. Signal Process..

[18]  Vladimir V. Lukin,et al.  Estimation of single-tone signal frequency by using the L-DFT , 2007, Signal Process..

[19]  Braham Barkat,et al.  Robust time-frequency distributions based on the robust short time fourier transform , 2005, Ann. des Télécommunications.

[20]  Shin'ichi Koike,et al.  Analysis of normalized correlation algorithm for adaptive filters in impulsive noise environments , 2011, 2011 19th European Signal Processing Conference.

[21]  Gonzalo R. Arce,et al.  A general weighted median filter structure admitting negative weights , 1998, IEEE Trans. Signal Process..

[22]  Douglas L. Jones,et al.  A signal-dependent time-frequency representation: optimal kernel design , 1993, IEEE Trans. Signal Process..