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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[[#refDJJ2007|[2]]], 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.[[#refDJJ2007|[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== |
| | * '''Gabor transform''' |
| | :<math> G_x(t,f) = \int_{-\infty}^\infty e^{-\pi(\tau-t)^2}e^{-j2\pi f\tau}x(\tau) \, d\tau </math> |
| | * '''Wigner distribution function''' |
| | :<math> W_x(t,f)=\int_{-\infty}^\infty x(t+\tau/2)x^*(t-\tau/2)e^{-j2\pi\tau\,f} \, d\tau</math> |
| | * '''Gabor–Wigner transform''' |
| | :There are many different combinations to define the Gabor–Wigner transform. Here four different definitions are given. |
| | # <math>D_x(t,f)=G_x(t,f)\times W_x(t,f)</math> |
| | # <math>D_x(t,f)=\min\left\{|G_x(t,f)|^2,|W_x(t,f)|\right\}</math> |
| | # <math>D_x(t,f)=W_x(t,f)\times \{|G_x(t,f)|>0.25\}</math> |
| | # <math>D_x(t,f)=G_x^{2.6}(t,f)W_x^{0.7}(t,f)</math> |
| | |
| | ==See also== |
| | * [[Time-frequency representation]] |
| | * [[Short-time Fourier transform]] |
| | * [[Gabor transform]] |
| | * [[Wigner distribution function]] |
| | |
| | ==References== |
| | * Jian-Jiun Ding, Time frequency analysis and wavelet transform class note, the Department of Electrical Engineering, National Taiwan University (NTU), Taipei, Taiwan, 2007. |
| | * <cite id=refDJJ2007>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</cite>. |
| | |
| | {{DEFAULTSORT:Gabor-Wigner transform}} |
| | [[Category:Integral transforms]] |
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[2], 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
- Wigner distribution function
- There are many different combinations to define the Gabor–Wigner transform. Here four different definitions are given.
See also
References
- 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.