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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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  <title>Spectral Estimation (LTPDA Toolbox)</title>
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  <p style="font-size:1px;">&nbsp;</p>

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  "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_diff.html"><img src="b_prev.gif" border="0" align=
      "bottom" alt="Discrete Derivative"></a>&nbsp;&nbsp;&nbsp;<a href=
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      "bottom" alt="Introduction"></a></td>
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  <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Spectral Estimation</h1>
  <hr>
  
  <p>
	<p>
  Spectral estimation is a branch of the signal processing, performed on data
  and based on frequency-domain techniques.<br>
  Within the LTPDA toolbox many functions of the Matlab <a href="matlab:doc('signal')" >Signal Processing Toolbox</a>
  (which is required) were rewritten to operate on LTPDA Analysis Objects.
  Univariate and multivariate technique are available, so to estimate for example the linear
  power spectral density or the cross spectral density of one or more signals. The focus of the
  tools is on time-series objects, whose spectral content needs to be estimated.<br>
  More detailed help on spectral estimation can also be found in the help associated with
  the Signal Processing Toolbox:
<table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
    <tr width="90%">
      <td>
       >> doc signal
      </td>
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      <td align="left" width="20"><a href="sigproc_diff.html"><img src=
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      "Discrete Derivative"></a>&nbsp;</td>

      <td align="left">Discrete Derivative</td>

      <td>&nbsp;</td>

      <td align="right">Introduction</td>

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      "sigproc_intro.html"><img src="b_next.gif" border="0" align=
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  <p class="copy">&copy;LTP Team</p>
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