How to compare two random signals
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I have two simulations of ship motion, and measured ship motion. Generally, the motion is random. I've been asked to compare the three sets of data. I was trying the goodness of fit (sum((fi-Fi)^2/Fi) where Fi is count in bins like histogram) and evaluating Fi using Chi Squared cdf to get the probability of being the different. I'm also comparing the mean and standard deviation to see if they are statistically the same. Are there other approaches to compare random data. I'd like to do something with the power spectrum, but what is the statistical comparison between two psd?
The signals are not recorded simultaneously, they represent ship motion in a similar seaway. So I think correlation and coherence don't apply.
Oh, maybe I misunderstood. You are suggesting to do corr on the PSD signals.
Edit>
I have a little trouble understanding some of the comments, so I'll expand on what I am doing. I have three sets of data, two are simulations, and one is measured. The data is ship motion, pitch, roll, heave displacements and rates. The basic question is are the two simulations similar, and use the measured data for reference. So, there are two random signals. The signals differ in duration, and also sample rate.
1) probability of same of mean and standard deviation
2) probability distribution function (histogram) of response with probability of same
4) comparison of autocorrelation of response
5) Comparison between frequency / spectral content of response
6) Quintile-quintile plot of two simulation, include slope and correlation coefficient of linear fit
I'm just starting to write up the results, but it appears that the QQ plot may be the best comparison. The frequency comparison is good to, and can potentially help guide resolution. The autocorrelation is also helpful, but I don't have experience in reading the figure.
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John BG
le 6 Mar 2016
John
is is possible to elaborate a bit?
1.- are you working with ship motion as in ship data: bearing speed locations?
or are you actually simulating and listening to ship movements?
2.- If you mean audio, then the mean and std (var) are the statistical moments of 1st and 2nd order
In Discrete Time Speech Processing J.Deller J.Hansen J.Proakis, in their basics review of stochastic processes, they pack the data in blocks, doesn't matter you only have one feed, and then find the covariance matrix:
3.- pg40 conditional expectation of y given event x.
cov_xy=xcorr_xy-mean_x*mean_y
If the signals have similar pdf then they would have distinctive xcorr(r1,r2),
4.- find out what moments are stationary
5.- pg56 find distance between signals,
6.- zero crossings
7.- Entropy: look for repeating chains
Réponses (3)
Star Strider
le 1 Mar 2016
Modifié(e) : Star Strider
le 1 Mar 2016
You may have actually answered your own question with the power spectral density idea. Comparing them by convolving them or convolving the inverse of one with the other in the complex frequency domain (a sort of transfer function calculation) could give you the information you want. That would be my first approach.
If they have different d-c offsets (mean values), that could be enough to give them different probability density functions. Binning each of them with histfit might give you some information.
EDIT — Comparing three sets of histogram data might be more appropriate for an analysis-of-variance than chi-squared.
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Image Analyst
le 1 Mar 2016
Can you take 3 signals, 2 that are similar and one that is different from those two, and use pwelch() to plot the spectrum, and attach your screenshot?
Can you take the moments (1st, 2nd, 3rd, 4th, 5th) of the spectrum and see how they change/differ?
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