Gabor–Wigner transform

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The Gabor transform, named after Dennis Gabor, and the Wigner distribution function, named after Eugene Wigner, are both tools for time-frequency analysis. Since the Gabor transform does not have high clarity, and the Wigner distribution function has a "cross term problem" (i.e. is non-linear), a 2007 study by S. C. Pei and J. J. Ding proposed a new combination of the two transforms that has high clarity and no cross term problem.[2] Since the cross term does not appear in the Gabor transform, the time frequency distribution of the Gabor transform can be used as a filter to filter out the cross term in the output of the Wigner distribution function.

Mathematical definition[edit]

  • Gabor transform
 G_x(t,f) = \int_{-\infty}^\infty e^{-\pi(\tau-t)^2}e^{-j2\pi f\tau}x(\tau) \, d\tau
  • Wigner distribution function
 W_x(t,f)=\int_{-\infty}^\infty x(t+\tau/2)x^*(t-\tau/2)e^{-j2\pi\tau\,f} \, d\tau
  • Gabor–Wigner transform
There are many different combinations to define the Gabor–Wigner transform. Here four different definitions are given.
  1. D_x(t,f)=G_x(t,f)\times W_x(t,f)
  2. D_x(t,f)=\min\left\{|G_x(t,f)|^2,|W_x(t,f)|\right\}
  3. D_x(t,f)=W_x(t,f)\times \{|G_x(t,f)|>0.25\}
  4. D_x(t,f)=G_x^{2.6}(t,f)W_x^{0.7}(t,f)

See also[edit]

References[edit]

  • Jian-Jiun Ding, Time frequency analysis and wavelet transform class note, the Department of Electrical Engineering, National Taiwan University (NTU), Taipei, Taiwan, 2007.
  • S. C. Pei and J. J. Ding, “Relations between Gabor transforms and fractional Fourier transforms and their applications for signal processing,” IEEE Trans. Signal Processing, vol. 55, no. 10, pp. 4839–4850, Oct. 2007.