Transformation between distributions in time–frequency analysis: Difference between revisions

Revision as of 03:30, 5 February 2010

Revision as of 03:33, 5 February 2010

Line 3:

In [[time-frequency analysis]], there should be a procedure to transform one distribution into another. It has been shown that a signal can recovered from a particular distribution if the kernel is not zero in a finite region. Given a distribution for which the signal can be recovered, the recovered signal can be taken to calculate any other distribution, so in these cases a relationship to expected to exist between them.

==General Classclass==

Only bilinear time-frequency representation, such as [[Wigner distribution function]] (WDF) and [[Cohen's class distribution function]], can be expressed as

: <math>C(t,\omega) = \dfrac{1}{4\pi^2}\iiint s^*(u-\dfrac{1}{2}\tau)s(u+\dfrac{1}{2}\tau)\phi(\theta,\tau)e^{-j\theta t-j\tau\omega+j\theta u}\, du\,d\tau\,d\theta</math> (1)

where <math>\phi(\theta,\tau)</math> is a two dimensional function called the kernel, which determines the distribution and its properties.

Line 13:

For the kernel of the [[Wigner distribution function]] (WDF) is one. However, it is no particular significance should be attached to that since it is to write the general form so that the kernel of any distribution is one, in which case the kernel of the [[Wigner distribution function]] (WDF) would be something else.

==Characteristic Functionfunction Formulationformulation==

The characteristic function is the double [[Fourier transform]] of the distribution. By inspection of Eq. (1), we can obtain that

: <math>C(t,\omega) = \dfrac{1}{4\pi^2}\iint M(\theta,\tau)e^{-j\theta t-j\tau\omega}\, d\theta\,d\tau</math> (2)

where

: <math>\begin{alignat}{2}

M(\theta,\tau) & = \phi(\theta,\tau)\int s^*(u-\dfrac{1}{2}\tau)s(u+\dfrac{1}{2}\tau)e^{j\theta u}\,du \\

& = \phi(\theta,\tau)A(\theta,\tau) \\

Line 27:

and where <math>A(\theta,\tau)</math> is the symmetrical ambiguity function. The characteristic function may be appropriately called the generalized ambiguity function.

==Transformation between Distributiondistribution==

To obtain that relationship suppose that there are two distributions, <math>C_1</math> and <math>C_2</math>, with corresponding kernels, <math>\phi_1</math> and <math>\phi_2</math>. Their characteristic functions are

: <math>M_1(\phi,\tau) = \phi_1(\theta,\tau)\int s^*(u-\dfrac{1}{2}\tau)s(u+\dfrac{1}{2}\tau)e^{j\theta u}\, du</math> (4)

: <math>M_2(\phi,\tau) = \phi_2(\theta,\tau)\int s^*(u-\dfrac{1}{2}\tau)s(u+\dfrac{1}{2}\tau)e^{j\theta u}\, du</math> (5)

Divide one equation by the other to obtain

: <math>M_1(\phi,\tau) = \dfrac{\phi_1(\theta,\tau)}{\phi_2(\theta,\tau)}M_2(\phi,\tau)</math> (6)

This is an important relationship because it connects the characteristic functions. For the division to be proper the kernel cannot to be zero in a finite region.

Line 43:

To obtain the relationship between the distributions take the double [[Fourier transform]] of both sides and use Eq. (2)

: <math>C_1(t,\omega) = \dfrac{1}{4\pi^2}\iint \dfrac{\phi_1(\theta,\tau)}{\phi_2(\theta,\tau)}M_2(\theta,\tau)e^{-j\theta t-j\tau\omega}\, d\theta\,d\tau</math> (7)

Now express <math>M_2</math> in terms of <math>C_2</math> to obtain

: <math>C_1(t,\omega) = \dfrac{1}{4\pi^2}\iiiint \dfrac{\phi_1(\theta,\tau)}{\phi_2(\theta,\tau)}C_2(t,\omega^')e^{j\theta(t^'-t)+j\tau(\omega^'-\omega)}\, d\theta\,d\tau\,dt^'\,d\omega^'</math> (8)

This relationship can be written as

: <math>C_1(t,\omega) = \iint g_{12}(t^'-t,\omega^'-\omega)C_2(t,\omega^')\,dt^'\,d\omega^'</math> (9)

with

: <math>g_{12}(t,\omega) = \dfrac{1}{4\pi^2}\iint \dfrac{\phi_1(\theta,\tau)}{\phi_2(\theta,\tau)}e^{j\theta t+j\tau\omega}\, d\theta\, d\tau</math> (10)

==Relation of the Spectrogramspectrogram to Otherother Bilinearbilinear Representationsrepresentations==

Now we specialize to the case where one transform from an arbitrary representation to the spectrogram. In Eq. (9), both <math>C_1</math> to be the spectrogram and <math>C_2</math> to be arbitrary are set. In addition, to simplify notation, <math>\phi_{SP} = \phi_1</math>, <math>\phi = \phi_2</math>, and <math>g_{SP} = g_{12}</math> are set and written as

: <math>C_{SP}(t,\omega) = \iint g_{SP}(t^'-t,\omega^'-\omega)C(t,\omega^')\,dt^'\,d\omega^'</math> (11)

The kernel for the spectrogram with window, <math>h(t)</math>, is <math>A_h(-\theta,\tau)</math> and therefore

: <math>\begin{alignat}{3}

g_{SP}(t,\omega) & = \dfrac{1}{4\pi^2}\iint \dfrac{A_h(-\theta,\tau)}{\phi(\theta,\tau)}e^{j\theta t+j\tau\omega}\, d\theta\,d\tau \\

& = \dfrac{1}{4\pi^2}\iiint \dfrac{1}{\phi(\theta,\tau)}h^*(u-\dfrac{1}{2}\tau)h(u+\dfrac{1}{2}\tau)e^{j\theta t+j\tau\omega-j\theta u}\, du\,d\tau\,d\theta \\

Line 73:

If taking the kernels for which <math>\phi(-\theta,\tau)\phi(\theta,\tau) = 1</math>, <math>g_{SP}(t,\omega)</math> is just the distribution of the window function, except that it is evaluated at <math>-\omega</math>. Therefore,

: <math>g_{SP}(t,\omega) = C_h(t,-\omega)</math> (13)

for kernels that satisfy <math>\phi(-\theta,\tau)\phi(\theta,\tau) = 1</math>

Line 79:

and

: <math>C_{SP}(t,\omega) = \iint C_s(t^',\omega^')C_h(t^'-t,\omega^'-\omega)\,dt^'\,d\omega^'</math> (14)

for kernels that satisfy <math>\phi(-\theta,\tau)\phi(\theta,\tau) = 1</math>

Line 85:

This was shown by Janssen[4]. For the case where <math>\phi(-\theta,\tau)\phi(\theta,\tau)</math> does not equal one, then

: <math>C_{SP}(t,\omega) = \iiiint G(t^{''},\omega^{''})C_s(t^',\omega^')C_h(t^{''}+t^'-t,-\omega^{''}+\omega-\omega^')\,dt^'\,dt^{''}\,d\omega^\,d\omega^{''}</math> (15)

where

: <math>G(t,\omega) = \dfrac{1}{4\pi^2}\iint \dfrac{e^{-j\theta t-j\tau\omega}}{\phi(\theta,\tau)\phi(-\theta,\tau)}\, d\theta\,d\tau</math> (16)

==References==