Calculating power spectral density from FFT

In summary, the conversation discusses using a RTL SDR stick for amateur radio astronomy at 1420MHz, hydrogen line. The user is interested in using a finished program, but wants to understand how it works. The program can receive I/Q data and there is a library for Raspberry Pi that can perform Fast Fourier Transform on the data. The user is unsure how to find the power spectral density from the FFT output, but it is explained that it can be calculated by taking the square of the amplitude data. It is also noted that if the FFT is performed on a real input signal, the positive and negative frequency parts have equal power and can be plotted by multiplying by 2.
  • #1
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9
EDIT: Sorry. It's FFT - Fast Fourier Transform, not FTT.
I am interested in doing some amateur radio astronomy. Mainly at 1420MHz, hydrogen line. I have a RTL SDR stick. For those who don't know what that is, it's USB DVB-T receiver that can receive anything between 24 – 1766 MHz.

Now, there is a finished program for what I am asking, but I don't like using something that I don't know how it works. This is the source for finished program https://github.com/mariocannistra/radio-astronomy-fftw

SDR stick outputs I/Q data. I/Q data is just amplitude data but the amplitude is sampled at the same time 90 degrees away from the other sample. Amplitude of the signal is sqrt(I^2+Q^2). (I don't know how to insert equation, sorry). No problem so far.

I can record a sample of the signal and there is a library for Raspberry PI for doing Fast Fourier Transform on that sample. This is where I got stuck. How to find power spectral density from the Fast Fourier Transform?

I got the impression that it's just amplitude squared - I^2+Q^2? Can it be that simple?

I apologize for any nonsense that I may have said here. And of course I wrote FTT instead of FFT in the thread title. If I wrote everything correctly that would be weird. Moderators, please correct that.
 
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  • #2
The FFT gives a complex output which is basically Z = I + Qi. So to generate the power spectrum you take Z * conj(Z) = abs(Z).^2 = I^2 + Q^2.
If you're taking the FFT of a real input signal, then the positive and negative frequency parts have equal power, so you can just plot the positive frequency power spectrum and multiply by 2.
 
  • #3
olivermsun said:
The FFT gives a complex output which is basically Z = I + Qi. So to generate the power spectrum you take Z * conj(Z) = abs(Z).^2 = I^2 + Q^2.
If you're taking the FFT of a real input signal, then the positive and negative frequency parts have equal power, so you can just plot the positive frequency power spectrum and multiply by 2.

I assume that in Z=I + Qi, I and Q are not the same variables as I, Q in raw data received from SDR? In this case Z= I + Qi, I and Q are just real and imaginary components of FFT output?
 
  • #4
Sorry for the bad notation. Yes, I and Q are real and imaginary components of the FFT output, but they are basically just the coefficients (not the full oscillating signals as in the raw I/Q data outputs).
 

FAQ: Calculating power spectral density from FFT

1. What is power spectral density (PSD)?

Power spectral density (PSD) is a measure of the power distribution of a signal over different frequencies. It is used to analyze the frequency components of a signal and is often used in signal processing and spectral analysis.

2. How is PSD calculated from FFT?

PSD is calculated from the Fast Fourier Transform (FFT) of a signal. The FFT converts a signal from its time domain to its frequency domain, and the PSD is then calculated by taking the squared magnitude of the FFT values.

3. What is the unit of PSD?

The unit of PSD depends on the unit of the original signal. If the signal is in the time domain, the unit of PSD will be the square of the unit of the signal. If the signal is in the frequency domain, the unit of PSD will be the square of the unit of frequency.

4. What is the significance of PSD in signal analysis?

PSD is significant in signal analysis because it provides information about the frequency components of a signal. It can help identify dominant frequencies and any underlying patterns or trends in the signal. PSD is also used in various fields, such as communication, audio processing, and vibration analysis.

5. Are there any limitations to using PSD for signal analysis?

Yes, there are limitations to using PSD for signal analysis. PSD assumes that the signal is stationary, meaning that its statistical properties do not change over time. If the signal is non-stationary, PSD may not accurately represent the signal's frequency components. Additionally, PSD does not provide information about the phase of the signal, which may be important in certain applications.

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