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Coherence MATLAB Resources

March 27, 2019

Transfer Function

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Scaled Transfer Function Estimator

Coherence

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Coherence MATLAB Resources

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Quiz: Mathematics for Understanding
Waveform Relationships

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MATLAB® offers operators supporting several statistical estimators using Welch’s method. These include the following:

   

mscohere(…)

Estimate ordinary coherence.

https://www.mathworks.com/help/signal/ref/mscohere.html

   

pwelch(…)

Estimate power spectral density.

https://www.mathworks.com/help/signal/ref/pwelch.html

   

cpsd(…)

Estimate cross power spectral density.

https://www.mathworks.com/help/signal/ref/cpsd.html

Example Code from MATLAB

Complex coherence can be calculated in MATLAB using cross-spectral density (CSD) and power spectral density (PSD) as demonstrated below.

  clear                              % clear all variables
  format long g;                     % set format of text output
  N = 16;                            % length of input data sequence x
  Fs = 1;                            % sample rate (used for scaling)
  percent = 50;                      % percent overlap
  Nfft = N * (1-percent/100);
  x= [0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0]';
  y= [0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0]';
%   0        3          7              11  13  15
%----------------------------------------------------------------------------
% smoothing window
%----------------------------------------------------------------------------
  window = hanning(Nfft);            % Hanning       window
%----------------------------------------------------------------------------
% calculate estimates
%----------------------------------------------------------------------------
[Sxy,freq]=cpsd(    x,y,window,Noverlap,Nfft,Fs);  % estimate Sxy
[Sxx,freq]=pwelch(  x,  window,Noverlap,Nfft,Fs);  % estimate Sxx
[Syy,freq]=pwelch(    y,window,Noverlap,Nfft,Fs);  % estimate Syy
[Cms,freq]=mscohere(x,y,window,Noverlap,Nfft,Fs);  % estimate ordinary coherence
% |      |    |     | |  |        |       |   |_____ sample rate
% |      |    |     | |  |        |       |_________ estimate length
% |      |    |     | |  |        |_________________ num. overlapping positions
% |      |    |     | |  |__________________________ window type
% |      |    |     | |_____________________________ data sequence y
% |      |    |     |_______________________________ data sequence x
% |      |    |_____________________________________ estimation operation
% |      |__________________________________________ x-axis sequence
% |_________________________________________________ estimate sequence
Cxy = Sxy ./ sqrt( Sxx .* Syy );                   %  estimate complex coherence
%----------------------------------------------------------------------------
% perform integrity check
%----------------------------------------------------------------------------
if norm(Cms-abs(Cxy).^2)<1e-12
   fprintf("Cxy integrity check OK!n");
else
   fprintf("Cxy integrity check FAILED!!!n");
end

A Note of Caution

As the name implies, estimators yield estimates. In general, these estimates contain some error with respect to the true value.

It would be difficult to find a more striking example of this situation than the K=1 Welch estimate of coherence. To understand this, compare the rectangular window K=1 Welch estimate of coherence with the true coherence of uncorrelated white noise signals:

If:

  1. x[n] and y[n] are zero-mean, white noise sequences
  2. x[n] and y[n] are uncorrelated
  3. the estimate Ĉxy (w) is the K=1 Welch estimate of x and y

Then:

(1)   \begin{equation*} \hat{C}_{xy}(\omega)=1 \end{equation*}

Proof:

(2)   \begin{equation*} |C(\omega)|=\left|\frac{S_{xy}}{\sqrt{S_{xx}S_{yy}}}\right| \end{equation*}

(3)   \begin{equation*} =\left|\frac{(\tilde{F}x)(\tilde{F}y)^*}{\sqrt{|\tilde{F}x|^2|\tilde{F}y|^2}}\right| \end{equation*}

(4)   \begin{equation*} =1 \end{equation*}

Thus, the K=1 Welch estimate of coherence is always 1!  This is definitely an estimate; but the question is, “Is it a good estimate?”

To answer this question, let’s compute the true (non-estimated) coherence:

(5)   \begin{equation*} |C_{xy}(\omega)|\triangleq|\tilde{F}[R_{xy}(m)]| \end{equation*}

 by definition of coherence Cxy(ω)

(6)   \begin{equation*} \triangleq|\tilde{F}\mathbf{E}[x(n)y(n+m)]| \end{equation*}

 by definition of cross-correlation Rxy

(7)   \begin{equation*} =|\tilde{F}(\mathbf{E}[x(n)]\mathbf{E}[y(n+m)])| \end{equation*}

 because x[n] and y[n] are uncorrelated

(8)   \begin{equation*} =|\tilde{F}(0\times0)| \end{equation*}

 because x[n] and y[n] have mean=0

(9)   \begin{equation*} Cxy(\omega)=0 \end{equation*}

 because the Fourier transform of 0 is 0

So, we see that the true value of the coherence is Cxy(ω)= 0 (the smallest possible value), but the Welch estimate is Ĉxy(ω)= 1 (the largest possible value). It is likely that, in most cases, Welch will yield a very “good” estimate. However, as the above example demonstrates, this is not always the case and some caution is in order.