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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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  <title>Cross-spectral density estimates (LTPDA Toolbox)</title>
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  <p style="font-size:1px;">&nbsp;</p>

  <table class="nav" summary="Navigation aid" border="0" width=
  "100%" cellpadding="0" cellspacing="0">
    <tr>
      <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td>

      <td valign="baseline" align="right"><a href=
      "sigproc_psd.html"><img src="b_prev.gif" border="0" align=
      "bottom" alt="Power spectral density estimates"></a>&nbsp;&nbsp;&nbsp;<a href=
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      "bottom" alt="Cross coherence estimates"></a></td>
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  <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Cross-spectral density estimates</h1>
  <hr>
  
  <p>
	<h2>Description</h2>
<p>
  Cross-power spectral density is performed by the Welch's averaged, modified periodogram method.
  The LTPDA method <a href="matlab:doc('ao/cpsd')">ao/cpsd</a> estimates the cross-spectral density of time-series
  signals, included in the input <tt>ao</tt>s following the Welch's averaged, modified periodogram method <a href="#references">[1]</a>. 
  Data are windowed prior to the estimation of the spectra, by multiplying
  it with a <a href="specwin.html">spectral window object</a>, and can be detrended by polinomial of time in order to reduce the impact
  of the border discontinuities. The window length is adjustable to shorter lenghts to reduce the spectral
  density uncertainties, and the percentage of subsequent window overlap can be adjusted as well.
  <br>
  <br>
  <h2>Syntax</h2>
</p>
<div class="fragment"><pre>
    <br>    b = cpsd(a1,a2,pl)
  </pre>
</div>
<p>
  <tt>a1</tt> and <tt>a2</tt> are the 2 <tt>ao</tt>s containing the input time series to be evaluated, <tt>b</tt> is the output object, 
  and <tt>pl</tt> is an optional parameters list.
  <h2>Parameters</h2>  
The parameter list <tt>pl</tt> includes the following parameters:</p>
<ul>
  <li> <tt>'Nfft'</tt> - number of samples in each fft [default: length of input data]
    A string value containing the variable 'fs' can
  also be used, e.g., plist('Nfft', '2*fs') </li>
 <li> <tt>'Win'</tt> - the window to be applied to the data to remove the 
    discontinuities at edges of segments. [default: taken from user prefs].<br>
    The window is described by a string with its name and, only in the case of Kaiser window,
  the additional parameter <tt>'psll'</tt>. <br>For instance: plist('Win', 'Kaiser', 'psll', 200).  </li>
  <li> <tt>'Olap'</tt> - segment percent overlap [default: -1, (taken from window function)] </li>
  <li> <tt>'Order'</tt> - order of segment detrending <ul>
      <li>      -1 - no detrending  </li>
      <li>       0 - subtract mean [default] </li>
      <li>       1 - subtract linear fit </li>
  <li>       N - subtract fit of polynomial, order N  </li> </ul> </li>
  <li><tt>'Navs'</tt>  - number of averages. If set, and if Nfft was set to 0 or -1, the number of points for each window will be calculated to match the request. [default: -1, not set] </li> 
 <li><tt>'Times'</tt>  - interval of time to evaluate the calculation on. If empty [default], it will take the whole section.</li>
</ul>
<p>
  The length of the window is set by the value of the parameter <tt>'Nfft'</tt>, so that the window
  is actually rebuilt using only the key features of the window, i.e. the name and, for Kaiser windows, the PSLL.
</p>

<p>As an alternative to setting the number of points <tt>'Nfft'</tt> in each window, it's possible to ask for a given number of CPSD estimates by setting the  <tt>'Navs'</tt> parameter, and the algorithm takes care of calculating the correct window length, according to the amount of overlap between subsequent segments.</p>
<p>
  <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
    <tr width="90%">
      <td>
        If the user doesn't specify the value of a given parameter, the default value is used.
      </td>
    </tr>
  </table>
</p>

<p>
  The function makes CPSD estimates between the 2 input <tt>ao</tt>s. The input argument
  list must contain 2 analysis objects, and the output will contain the CPSD estimate.
  If passing two identical objects <tt>ai</tt>, the output will be equivalent to the output of <tt>psd(ai)</tt>.
</p>
</pre> </div>
</p>
<p>
  <h2>Algorithm</h2>
  <p>
    The algorithm is based in standard MATLAB's tools, as the ones used by <a href="matlab:doc('pwelch')">pwelch</a>. However, in order to 
    compute the standard deviation of mean for each frequency bin, the averaging of the different segments is performed using Welford's 
    algorithm  <a href="#references">[2]</a> which allows to compute mean and variance in one loop.   
  </p>  
  <h2>Example</h2>
</p>
<p>
  Evaluation of the CPSD of two time-series represented by: a low frequency sinewave signal superimposed to
  white noise, and a low frequency sinewave signal at the same frequency, phase shifted and with different
  amplitude, superimposed to white noise.
</p>
<div class="fragment"><pre>
    nsecs = 1000;
    x = ao(plist(<span class="string">'waveform'</span>,<span class="string">'sine wave'</span>,<span class="string">'f'</span>,0.1,<span class="string">'A'</span>,1,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,10)) + ...
    ao(plist(<span class="string">'waveform'</span>,<span class="string">'noise'</span>,<span class="string">'type'</span>,<span class="string">'normal'</span>,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,10));
    x.setYunits(<span class="string">'m'</span>);
    y = ao(plist(<span class="string">'waveform'</span>,<span class="string">'sine wave'</span>,<span class="string">'f</span>',0.1,<span class="string">'A'</span>,2,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,10,<span class="string">'phi'</span>,90)) + ...
    4*ao(plist(<span class="string">'waveform'</span>,<span class="string">'noise'</span>,<span class="string">'type'</span>,<span class="string">'normal'</span>,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,10));
    y.setYunits(<span class="string">'V'</span>);
    z = cpsd(x,y,plist(<span class="string">'nfft'</span>,1000));
    iplot(z);
  </pre>
</div>

<img src="images/cpsd_1.png" alt="" border="3">
<br>

<h2><a name="references">References</a></h2>

<ol>
  <li> P.D. Welch, The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short,
Modified Periodograms, <i>IEEE Trans. on Audio and Electroacoustics</i>, Vol. 15, No. 2 (1967), pp. 70 - 73</a></li>
  <li> B. P. Weldford, Note on a Method for Calculating Corrected Sums of Squares and Products,
  <i>Technometrics<i>, Vol. 4, No. 3 (1962), pp 419 - 420.</li>
</ol>





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  <br>
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      <td align="left" width="20"><a href="sigproc_psd.html"><img src=
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      "Power spectral density estimates"></a>&nbsp;</td>

      <td align="left">Power spectral density estimates</td>

      <td>&nbsp;</td>

      <td align="right">Cross coherence estimates</td>

      <td align="right" width="20"><a href=
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  <p class="copy">&copy;LTP Team</p>
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