Confused about autocorrelation and PSD

In summary, The conversation discusses a property that states the expected value of the Fourier transformed signal is proportional to the spectral density function (PSD) S_{XX}(\omega). This is defined as the integral of the autocorrelation function r_{XX}(\tau) multiplied by e^{-i\omega\tau} over all values of \tau. It is suggested that this may be related to the Wiener-Khintchine theorem.
  • #1
xaratustra
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I am confused a bit :confused:. I read in a paper that the following property holds, but can't find where it comes from.

[itex]\mathsf{E}\left[X(\omega)X^*(\omega')\right]=2\pi\delta(\omega-\omega')S_{XX}(\omega)[/itex]

it says that the expected value of the Fourier transformed signal is proportional to the spectral density function (PSD) [itex]S_{XX}(\omega)[/itex] which is as usual defined as:

[itex]S_{XX}(\omega)=\int_{-\infty}^{\infty}r_{XX}(\tau)e^{-i\omega\tau}\,d\tau[/itex]

Any one knows where this comes from?

thanks.
 
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Related to Confused about autocorrelation and PSD

1. What is autocorrelation and how is it related to PSD?

Autocorrelation refers to the correlation of a signal with a delayed version of itself. It is related to PSD (Power Spectral Density) because the PSD is a measure of the power of a signal as a function of frequency, and it can be calculated from the autocorrelation function using the Fourier transform.

2. How does autocorrelation affect signal processing and analysis?

Autocorrelation can provide important information about the structure and characteristics of a signal. In signal processing, it is commonly used to detect periodic patterns and to identify the presence of noise. In analysis, it can help to reveal underlying trends and patterns in data, and is often used in time series analysis.

3. What is the difference between autocorrelation and cross-correlation?

Autocorrelation refers to the correlation of a signal with a delayed version of itself, while cross-correlation involves finding the correlation between two different signals. Autocorrelation is used to analyze the properties of a single signal, while cross-correlation is used to compare the similarities between two signals.

4. How can autocorrelation be calculated and interpreted?

Autocorrelation can be calculated by taking the signal and correlating it with a time-delayed version of itself. The resulting values can then be plotted on an autocorrelation function. The shape and magnitude of the autocorrelation function can provide information about the periodicity and characteristics of the signal.

5. How does autocorrelation affect the power spectrum of a signal?

The power spectrum of a signal is directly related to its autocorrelation function. A signal with strong autocorrelation will have a power spectrum with sharp peaks at specific frequencies, while a signal with weak autocorrelation will have a smoother power spectrum. Additionally, the autocorrelation function can be used to remove noise from a signal in order to better analyze its power spectrum.

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