Generalized polynomial wigner spectrogram for high-resolution time-frequency analysis

A good time-frequency (TF) analysis method should have the advantages of high clarity and no cross term. However, there is always a trade-off between the two goals. In this paper, we propose a new TF analysis method, which is called the generalized polynomial Wigner spectrogram (GPWS). It combines the generalized spectrogram (GS) and the polynomial Wigner-Ville distribution (PWVD). The PWVD has a good performance for analyzing the instantaneous frequency of a high order exponential function. However, it has the cross term problem in the multiple component case. By contrast, the GS can avoid the cross term problem, but its clarity is not enough. The proposed GPWS can combine the advantages of the PWVD and the GS. It can achieve the goals of high clarity, no cross term, and less computation time simultaneously. We also perform simulations to show that the proposed GPWS has better resolution than other TF analysis methods.

[1]  Joerg F. Hipp,et al.  Time-Frequency Analysis , 2014, Encyclopedia of Computational Neuroscience.

[2]  S. Qian,et al.  Joint time-frequency analysis : methods and applications , 1996 .

[3]  W. L. Cowley The Uncertainty Principle , 1949, Nature.

[4]  Paolo Boggiatto,et al.  Two-Window Spectrograms and Their Integrals , 2009 .

[5]  G. Folland,et al.  The uncertainty principle: A mathematical survey , 1997 .

[6]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  Braham Barkat,et al.  Design of higher order polynomial Wigner-Ville distributions , 1999, IEEE Trans. Signal Process..

[9]  Paolo Boggiatto,et al.  Uncertainty principle, positivity and Lp-boundedness for generalized spectrograms , 2007 .

[10]  H. P. Robertson The Uncertainty Principle , 1929 .

[11]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

[12]  Srdjan Stankovic,et al.  An analysis of instantaneous frequency representation using time-frequency distributions-generalized Wigner distribution , 1995, IEEE Trans. Signal Process..

[13]  Boualem Boashash,et al.  Polynomial Wigner-Ville distributions , 1995, Optics & Photonics.

[14]  M. Bastiaans,et al.  Gabor's expansion of a signal into Gaussian elementary signals , 1980, Proceedings of the IEEE.

[15]  Franz Franchetti,et al.  Generating high performance pruned FFT implementations , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[17]  Thomas F. Quatieri,et al.  Short-time Fourier transform , 1987 .

[18]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  S. Qian,et al.  Joint time-frequency analysis , 1999, IEEE Signal Process. Mag..

[20]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[21]  L. Cohen Generalized Phase-Space Distribution Functions , 1966 .

[22]  Karlheinz Gröchenig,et al.  Foundations of Time-Frequency Analysis , 2000, Applied and numerical harmonic analysis.

[23]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .