Find Frequencies in Signal with Random Sample Rate

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In summary, the conversation discusses finding frequencies in a signal obtained by random sampling, and the use of FFT and LSSA for spectral analysis. The speaker also mentions the importance of timing in determining accurate frequency estimates. The conversation ends with the speaker mentioning contacting the manufacturer for potential changes in sample rate and the current periodic timing of the measurements.
  • #1
liquidFuzz
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Hi

How do I find the frequencies in a signal obtained by samplings with a random sample rate? Normally I use a fft analysis, but then I have a fixed sample rate.

Thanks!
 
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  • #2
I don;t understand the questions
Do you mean that you don't know the sample rate?
Or that the signal was sampled at random intervals?
Or that it was sampled at random intervals but you know WHEN the measurement was done (i,e., each sample has a time tag)?

Regardless, you obviously need SOME form of information about the timing of the measurement to estimate the frequency.
 
  • #3
If you use a random sampling method then it must always be faster than the Nyquist rate. If some samples are spaced longer than the Nyquist interval then there will be random errors on the recovered signal. This amounts to a source of noise which accompanies the signal, and amounts to quantisation noise. Any sampling below the Nyquist rate will produce gross distortion.
 
  • #4
If you're talking about data sampled with random intervals (as opposed to constant intervals where you'd use the FFT), then instead of FFT, you want to use LSSA. There's an efficient implementation in Numerical Recipes in C (I don't have it on me or I would give you the chapter number).

Other implementations:
Scipy
Matlab

LSSA is recommended when you expect a periodic signal. I've looked in the literature before but never found answers about how it performs for spectral analysis of non-periodic signals (noise power spectra, for example).
 
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Likes berkeman
  • #5
Time is of the essence. What do you know about the timing of your sampling?
 
  • #6
Thanks for all the input! I'll check the numerical approach suggested.

So far I only really tried to attack the issue by contacting the manufacturer of the measuring device I'm tinkering with to see if I can change the sample rate.
 
  • #7
Oh, i forgot mentioning. The timing is periodic atm. 0,335 and 0,408 between samples. The 0,335 is used 5 times for each 0,408 timing.
 
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Likes Twigg

1. What is a signal with random sample rate?

A signal with random sample rate refers to a type of signal that has varying intervals between each sample. This means that the data points in the signal are not evenly spaced, making it difficult to analyze and extract information from.

2. How do you find frequencies in a signal with random sample rate?

Finding frequencies in a signal with random sample rate can be challenging, but it can be done using a technique called interpolation. This involves estimating the missing data points between the samples to create a more evenly spaced signal, which can then be analyzed using traditional frequency analysis methods.

3. What are some challenges in finding frequencies in a signal with random sample rate?

One of the main challenges in finding frequencies in a signal with random sample rate is the irregular spacing between data points, which can lead to inaccurate frequency estimations. Additionally, the presence of noise in the signal can also make it more difficult to identify the true frequencies.

4. Are there any specific techniques or algorithms for finding frequencies in a signal with random sample rate?

Yes, there are various techniques and algorithms that have been developed specifically for analyzing signals with random sample rate. Some examples include the Lomb-Scargle periodogram and the weighted Lomb-Scargle periodogram, which are commonly used in astronomy and geophysics.

5. How can the results of finding frequencies in a signal with random sample rate be used?

The results of finding frequencies in a signal with random sample rate can be used in a variety of applications, such as identifying patterns or trends in the data, detecting anomalies or abnormalities, and predicting future behavior of the signal. This information can be valuable in fields such as signal processing, data analysis, and machine learning.

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