Units For a Power Spectral Density (PSD)

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Discussion Overview

The discussion revolves around the units for Power Spectral Density (PSD) in the context of Fourier Transforms applied to time series data from various sensors, specifically pressure and accelerometer sensors. Participants explore the relationship between the units of the time series and the resulting PSD, seeking a general understanding of how to interpret and compute these units.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant seeks clarification on the units of the Fourier Transform, suggesting that if a time series has units ##U##, then its Fourier Transform would have units of ##U/Hz##.
  • Another participant emphasizes the importance of understanding the equations defining Fourier transforms and PSDs, implying that the units derive from these equations.
  • A participant mentions a conversation with their graduate mentor, who provided an equation for PSD involving the Fourier Transform, sample length, and sample rate, concluding that the units for PSD derived from pressure would be ##Pa^2/Hz##.
  • One participant explains the units of the Fourier Transform for a voltage signal, detailing how the units of the power spectrum relate to physical power and suggesting that the PSD can be interpreted as the density of fluctuating power per frequency.
  • There is a discussion about the potential to relate wave height to energy using a proposed equation, with uncertainty expressed about how freely one can make such declarations regarding units.
  • Another participant raises a question about using dB for pressure sensors, referencing the computation of sound wave PSDs and seeking clarification on whether a similar approach could be applied in their case.

Areas of Agreement / Disagreement

Participants express varying levels of understanding regarding the units of PSD and the relationship between time series data and physical power. There is no consensus on the best approach to generalize the units across different types of sensors, and multiple competing views remain regarding the interpretation of PSD in different contexts.

Contextual Notes

Participants note the need for a clear understanding of how to relate the amplitude of time series to power, as well as the implications of using different units depending on the type of sensor data being analyzed. There are unresolved questions about the flexibility in defining relationships between quantities like wave height and energy.

person123
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(Just as clarification on what I am doing, I am attempting to use MATLAB to create PSD plots of time series from pressure and accelerometer sensors. Wave gauge sensors might still be helpful for me. I did not discuss accelerometer sensors in the post but maybe I could figure it out if I get the basic idea).

At the very least, I would like to make sure I am correct that the Fourier Transform of a time series with some units ##U## (##U## referring to any arbitrary unit), would be ##U/Hz## (so ##Us##). If this is wrong, then I think I have a fundamental misunderstanding.

I'm trying to understand Power Spectral Distributions; I think my confusion can largely be reduced to a question of units. I am particularly concerned with the PSD created from time series of wave height and pressure, but I think a general understanding of a PSD would be useful. From what I understand, the units for the y-axis of a PSD depends on the time series from which the PSD was created. On this Stack Overflow thread, someone states that a PSD of ##V^2/Hz## can be used when the time series is voltage. I think this make sense because ##P=V^2/R##.

I don't understand how I would extend this to other cases. I understand that there should be some way of relating the amplitude of the time series to the power. I don't quite understand how much freedom I have when doing this. Could I for example, just declare the following: $$E=\rho g H^2$$ where ##H## is the wave height? In that case ##E## would be some quantity which in some is sense associated with energy. As energy is dimensionally equivalent to power divided by frequency, and ##\rho## and ##g## are constants, could I simply square the Fast Fourier Transform and just let the y-axis be ##H^2## with the understanding that there's a clear relation to power?

In the case of pressure sensors, I am a bit more confused. I do understand that sound waves are pressure waves and the PSDs for sound waves use dB as the y axis. This is computed by ##\log_{10} (P/P_0)## (multiplied by 10 but I believe this is a detail in this context). Could I do something similar in my case?
 
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Do you know the equations defining Fourier transforms and the power spectral density? The units fall directly out of those equations.
 
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boneh3ad said:
Do you know the equation defining Fourier transforms and the power spectral density? The units fall directly out of those equations.
When I started this thread I did not. However, I ended up asking my graduate mentor this question, and I believe his answer directly relates to your question. He said the equation was the following: $$PSD=Y Y^* L \Delta t$$ ##Y## is the Fourier Transform, ##L## is the sample length, and ##\Delta t## is the sample rate. He also said that the units of the Fourier Series itself (for pressure say) would simply be ##Pa##. Therefore, the PSD would be ##Pa^2/Hz## I believe. This is what I used in my code. I still don't really have a true understanding of why you do this, but I think my understanding is good enough to create functions based on this. If you or anyone else could give me (or refer me to) an intuitive understanding of how this relates to power, I would greatly appreciate it.
 
That looks like you are at least on the right track. Consider that you have some time series, ##y(t)##. At this point, it has arbitrary units. As you've stated, it has a Fourier transform defined as
Y(f) = \mathscr{F}[y(t)](f) = \int\limits_{-\infty}^{\infty}y(t)\exp[-2\pi i f t]\;dt.
The power spectral density of ##y(t)## is then defined as
S_{yy}(f) = \lim_{T\to\infty}\dfrac{1}{T}E[Y(f)Y^*(f)],
where ##E[\cdot]## is the expectation operator and ##^*## denotes a complex conjugate.

So then let's look at the units assuming our signal is a raw voltage. The units of ##Y(f)## are
[Y(f)] = [\mathrm{V}\cdot \mathrm{s}]
since the exponential term is unitless, ##y(t)## has units of volts, and ##dt## has units of seconds. The units of the power spectrum are therefore
[S_{yy}(f)] = \left[\dfrac{1}{\mathrm{s}}\right][\mathrm{V}\cdot \mathrm{s}][\mathrm{V}\cdot \mathrm{s}] = [\mathrm{V}^2\cdot \mathrm{s}] = \left[ \dfrac{\mathrm{V}^2}{\mathrm{Hz}} \right].
If your measured signal ##y(t)## happens to be a different unit, just substitute that for volts. When it's a voltage signal, it's pretty clear how the power spectral density is related to actual physical power: ##S_{yy}## is literally the density of fluctuating power per frequency of the voltage signal. If you have a calibrated quantity (e.g. Pascals), it's related to the actual physical power by a calibration curve. Otherwise, you needn't always relate it to a physical power. You can just interpret it as the signal power instead of actual, physical Watts.

A good source:
[1] Bendat JS, Piersol AG. 2011. "Random Data." 4th Ed. Wiley.
 
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