FFTs, and ratio of FFTs Phase question

In summary, the conversation discusses how to find the phase between an input and output signal for system identification using swept-sin (chirp) signals. The magnitude of the frequency-domain transfer function can be found by taking the ratio of the output and input FFTs. To find the phase, the angle between the complex FFTs is taken. However, when using simulated constant-phase-shifted chirps, the phase is not a constant -140 degrees as expected. Instead, it drops dramatically near 1 Hz and goes towards -5000 degrees above 10 Hz. The reason for this is unclear and further investigation is needed.
  • #1
jblc
10
0
Q: How do you find the phase between an input and output signal? These signals are swept-sin (chirp: https://en.wikipedia.org/wiki/File:Linear-chirp.svg) signals for system identification, so I'm looking to find a transfer function.

Background: A frequency-domain Transfer Function's magnitude is found by taking the ratio of the output/input FFTs:
FFTratio = Complex{FFT out} / Complex{FFTin}, ∴ Magnitude = abs(FFTratio).
To find the phase, take the angle between the complex FFTs:
atan2( Imag{FFTratio}, Real{FFTratio} )

As a test, in Matlab's System Identification Tool, with two simple, 140 deg shifted and noisy 10 Hz sinusoids -- NON-swept, just simple sines -- the answer is as expected, and the phase is appr. -140 deg at 10 Hz in the phase plot.

Question: BUT when using two simulated constant-phase-shifted chirps, for system identification (chirp), the phase isn't a constant -140 Hz.
The phase drops dramatically from -140 deg near 1 Hz, and above 10 Hz it goes towards -5000 deg. See the attached images. The chirps are 0.01 Hz sinusoid at t=0, and a 400 Hz sinusoid at t=200s.
A zoomed 20s signal is shown for clarity. yc is output (top), uc is input (bottom).

Why is the phase not a constant -140 deg up until ~400 Hz? Why does the phase drop to -5000 deg? The swept-sin (chirp) peaks continue to remain at a constant phase relative to each other, so it should stay at -140

Attachments:
2x time-signals
1x FFTs of output(top) and input (bottom), called "Periodogram"
1x transfer function estimate, magnitude on top, phase on bottom
 

Attachments

  • chirp t full.jpg
    chirp t full.jpg
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  • chirp t.jpg
    chirp t.jpg
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  • chirp FFTs.jpg
    chirp FFTs.jpg
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  • chirp phase 2.jpg
    chirp phase 2.jpg
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  • #2
I'm sorry you are not generating any responses at the moment. Is there any additional information you can share with us? Any new findings?
 

Related to FFTs, and ratio of FFTs Phase question

1. What is an FFT?

An FFT (Fast Fourier Transform) is a mathematical algorithm used to convert a time-domain signal into its frequency-domain representation. This allows for the analysis and manipulation of signals in the frequency domain, which is useful in a variety of scientific fields including audio and image processing, communication systems, and physics.

2. How does an FFT work?

An FFT works by breaking down a signal into smaller chunks, applying mathematical operations to each chunk, and then recombining them to create the frequency-domain representation. It uses complex numbers and the Discrete Fourier Transform to perform these operations quickly and efficiently.

3. What is the significance of the ratio of FFTs phase?

The ratio of FFTs phase refers to the relationship between the phase angles of two signals in the frequency domain. It is often used in signal processing to compare the similarity or difference between two signals. For example, in audio mixing, the phase ratio can help determine if two signals are in phase (aligned) or out of phase (cancelling each other out).

4. Can an FFT be used for non-periodic signals?

Yes, an FFT can be used for both periodic and non-periodic signals. However, for non-periodic signals, it is important to use a windowing function to avoid distortions in the frequency-domain representation due to the finite length of the signal. A windowing function is a mathematical operation that reduces the effects of the signal's edges on the FFT calculation.

5. Are there any limitations to using an FFT?

Yes, there are some limitations to using an FFT. One limitation is that it assumes the input signal is stationary (constant over time). If the signal is changing over time, multiple FFTs may need to be taken to capture the changing frequency components. Additionally, the FFT is most accurate when the input signal is sampled at equally spaced intervals, so it may not be suitable for signals with irregular sampling. There are also limitations in terms of the resolution and accuracy of the frequency-domain representation, which can be influenced by factors such as the length of the signal and the chosen windowing function.

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