Jump to content

Transformation between distributions in time–frequency analysis

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by SmackBot (talk | contribs) at 01:59, 6 February 2010 (Date maintenance tags and general fixes: build 398:). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

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 class

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

(1)

where is a two dimensional function called the kernel, which determines the distribution and its properties.

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 function formulation

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

(2)

where

(3)

and where is the symmetrical ambiguity function. The characteristic function may be appropriately called the generalized ambiguity function.

Transformation between distribution

To obtain that relationship suppose that there are two distributions, and , with corresponding kernels, and . Their characteristic functions are

(4)
(5)

Divide one equation by the other to obtain

(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.

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

(7)

Now express in terms of to obtain

(8)

This relationship can be written as

(9)

with

(10)

Relation of the spectrogram to other bilinear representations

Now we specialize to the case where one transform from an arbitrary representation to the spectrogram. In Eq. (9), both to be the spectrogram and to be arbitrary are set. In addition, to simplify notation, , , and are set and written as

(11)

The kernel for the spectrogram with window, , is and therefore

(12)

If taking the kernels for which , is just the distribution of the window function, except that it is evaluated at . Therefore,

(13)

for kernels that satisfy

and

(14)

for kernels that satisfy

This was shown by Janssen[4]. For the case where does not equal one, then

(15)

where

(16)

References

[1] L. Cohen, "TIME-FREQUENCY ANALYSIS," Prentice-Hall, New York, 1995.

[2] L. Cohen, "Generalized phase-space distribution functions," Jour. Math. Phys., vol.7, pp. 781–786, 1966.

[3] L. Cohen, "Quantization Problem and Variational Principle in the Phase Space Formulation of Quantum Mechanics," Jour. Math. Phys., vol.7, pp. 1863–1866, 1976.

[4] A. J. E. M. Janssen, "On the locus and spread of pseudo-density functions in the time frequency plane," Philips Journal of Research, vol. 37, pp. 79–110, 1982.