How Do You Calculate the Magnitude of a Power Spectral Density Function?

In summary, the homework statement is to find the power spectral density only. The Attempt at a Solution is to use Fourier series and find the height of the delta functions.
  • #1
perplexabot
Gold Member
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5

Homework Statement


question.png

For now I am trying to find the power spectral density only.

The Attempt at a Solution


  • From the given graph, I got g(t) = -1 + 2rect(t/2), with a perdiod of 4s
  • PSD = Sg(f) = lim(T [itex]\rightarrow\infty[/itex] ) [itex]\frac{1}{2T}[/itex] || G(f) || [itex]^{2}[/itex]
  • My Fourier transform of g(t), G(f), I got to = - [itex]\delta[/itex] (f) + 4sinc(2f)
where " [itex]\delta[/itex] (f)" is the unit impulse function.
I want to continue but I am not sure this is correct? If it is correct, I need to get the magnitude and square it. I don't know why I am not too comfortable with magnitude. So in this case i have || - [itex]\delta[/itex] (f) + 4sinc(2f) || which I know ≠ ||- [itex]\delta[/itex] (f) || + || 4sinc(2f) ||. So what do I do? How do I find magnitude?

EDIT: Can this post be moved to the electrical engineering section? I feel it is more likely to get a reply there.
 
Last edited:
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  • #2
You know from Fourier series that the psd must be a sequence of delta functions situated at odd harmonics of the fundamental frequency (0.25 Hz). Compute the height of the delta functions as you were taught.

You can see by inspection what the dc component is and also the average power.

For the autocorrelation function I will give you a hint: it's a triangular function in tau space. You can obtain this function graphically if you shift the square wave against itself and compute the product for each shift position.
 
  • #3
You're already in the proper forum (engineering etc.).
 
  • #4
rude man said:
You know from Fourier series that the psd must be a sequence of delta functions situated at odd harmonics of the fundamental frequency (0.25 Hz). Compute the height of the delta functions as you were taught.

You can see by inspection what the dc component is and also the average power.

For the autocorrelation function I will give you a hint: it's a triangular function in tau space. You can obtain this function graphically if you shift the square wave against itself and compute the product for each shift position.

Thank you for your help. I was able to do it. For anyone that wants to know how, here is what you have to use:

PSD = ∑ |Cn|[itex]^{2}[/itex] δ(f-nf[itex]_{0}[/itex])
Cn =(1/T[itex]_{0}[/itex]) G(f)|[itex]^{for f = nf_{0}}[/itex]
Extract a single period from g(t) to get a new g(t) = -rect(t/4) + 2rect(t/2)

Then solve for PSD .
 
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  • #5




Hello, thank you for your question. It seems like you are on the right track in finding the power spectral density. The formula you have written for PSD is correct, and your Fourier transform of g(t) also appears to be correct. To find the magnitude, you can use the Pythagorean theorem to calculate the square root of the sum of the squares of the real and imaginary parts of your Fourier transform. In this case, it would be || - \delta (f) + 4sinc(2f) || = √((-1)^2 + (4sinc(2f))^2). This will give you the magnitude of your PSD. I hope this helps. If you have any further questions, please let me know.
 

1. What is power spectral density (PSD)?

Power spectral density is a mathematical representation of the distribution of power in a signal over different frequencies. It is a useful tool for analyzing signals in various fields such as signal processing, physics, and engineering.

2. How is PSD calculated?

PSD is typically calculated by taking the Fourier transform of a signal and then squaring its magnitude. The result is then normalized by the total signal power and frequency resolution to obtain the power spectral density.

3. What is the importance of PSD in signal analysis?

PSD provides valuable information about the frequency content and power distribution of a signal, making it a useful tool for analyzing and understanding complex signals. It also allows for comparison of different signals or different segments of a signal.

4. How is PSD used in practical applications?

PSD is used in a variety of applications, including noise reduction, filtering, and signal classification. It is also used in fields such as telecommunications, geology, and astronomy to analyze and interpret signals and data.

5. Can PSD be used to predict future behavior of a signal?

No, PSD cannot be used to predict the future behavior of a signal. It is a representation of the power distribution of a signal at a specific point in time and does not provide any information about the future behavior of the signal.

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