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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 18:04:34 +0100
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1 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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2 "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd">
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3
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4 <html lang="en">
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5 <head>
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6 <meta name="generator" content=
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7 "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org">
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8 <meta http-equiv="Content-Type" content=
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9 "text/html; charset=us-ascii">
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10
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11 <title>Power spectral density estimates (LTPDA Toolbox)</title>
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12 <link rel="stylesheet" href="docstyle.css" type="text/css">
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13 <meta name="generator" content="DocBook XSL Stylesheets V1.52.2">
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14 <meta name="description" content=
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15 "Presents an overview of the features, system requirements, and starting the toolbox.">
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16 </head>
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17
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18 <body>
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19 <a name="top_of_page" id="top_of_page"></a>
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20
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21 <p style="font-size:1px;">&nbsp;</p>
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22
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23 <table class="nav" summary="Navigation aid" border="0" width=
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24 "100%" cellpadding="0" cellspacing="0">
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25 <tr>
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26 <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td>
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27
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28 <td valign="baseline" align="right"><a href=
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29 "sigproc_methods.html"><img src="b_prev.gif" border="0" align=
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30 "bottom" alt="Spectral Estimation Methods"></a>&nbsp;&nbsp;&nbsp;<a href=
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31 "sigproc_cpsd.html"><img src="b_next.gif" border="0" align=
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32 "bottom" alt="Cross-spectral density estimates"></a></td>
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33 </tr>
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34 </table>
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35
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36 <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Power spectral density estimates</h1>
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37 <hr>
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38
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39 <p>
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40 <h2>Description</h2>
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41 <p>
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42 The LTPDA method <a href="matlab:doc('ao/psd')">ao/psd</a> estimates the power spectral density of time-series
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43 signals, included in the input <tt>ao</tt>s following the Welch's averaged, modified periodogram method <a href="#references">[1]</a>.
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44 Data are windowed prior to the estimation of the spectrum, by multiplying
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45 it with a <a href="specwin.html">spectral window object</a>, and can be detrended by a polinomial of time in order to reduce the impact
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46 of the border discontinuities. The window length is adjustable to shorter lenghts to reduce the spectral
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47 density uncertainties, and the percentage of subsequent window overlap can be adjusted as well. The detrending is
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48 performed on the individual windows. The user can choose the quantity being given in output among
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49 ASD (amplitude spectral density), PSD (power spectral density), AS (amplitude spectrum), and PS (power spectrum).
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50 <br>
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51 <br>
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52 <h2>Syntax</h2>
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53 </p>
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54 <div class="fragment"><pre>
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55 <br> bs = psd(a1, a2, a3, ..., pl)
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56 bs = psd(as, pl)
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57 bs = as.psd(pl)
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58 </pre> </div>
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59 <p>
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60 <tt>a1</tt>, <tt>a2</tt>, <tt>a3</tt>, ... are <tt>ao</tt>(s) containing the input time series to be evaluated. <tt>bs</tt> includes
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61 the output object(s) and <tt>pl</tt> is an optional parameter list.
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62 </p>
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63 <h2>Parameters</h2>
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64 <p>
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65 The parameter list <tt>pl</tt> includes the following parameters:
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66 </p>
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67 <ul>
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68 <li> <tt>'Nfft'</tt> - number of samples in each fft [default: length of input data]
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69 A string value containing the variable 'fs' can
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70 also be used, e.g., plist('Nfft', '2*fs') </li>
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71 <li> <tt>'Win'</tt> - the window to be applied to the data to remove the
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72 discontinuities at edges of segments. [default: taken from user prefs].<br>
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73 The window is described by a string with its name and, only in the case of Kaiser window,
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74 the additional parameter <tt>'psll'</tt>. <br>For instance: plist('Win', 'Kaiser', 'psll', 200). </li>
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75 </li>
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76 <li> <tt>'Olap'</tt> - segment percent overlap [default: -1, (taken from window function)] </li>
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77 <li> <tt>'Scale'</tt> - scaling of output. Choose from: <ul>
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78 <li> 'ASD' - amplitude spectral density </li>
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79 <li> 'PSD' - power spectral density [default] </li>
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80 <li> 'AS' - amplitude spectrum </li>
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81 <li> 'PS' - power spectrum </li> </ul> </li>
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82 <li> <tt>'Order'</tt> - order of segment detrending <ul>
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83 <li> -1 - no detrending </li>
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84 <li> 0 - subtract mean [default] </li>
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85 <li> 1 - subtract linear fit </li>
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86 <li> N - subtract fit of polynomial, order N </li> </ul> </li>
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87 <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>
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88 <li><tt>'Times'</tt> - interval of time to evaluate the calculation on. If empty [default], it will take the whole section.</li>
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89 </ul>
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90 <p>
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91 The length of the window is set by the value of the parameter <tt>'Nfft'</tt>, so that the window
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92 is actually built using only the key features of the window: the name and, for Kaiser windows, the psll.
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93 </p>
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94 <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 PSD 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>
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95 <p>
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96 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
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97 <tr width="90%">
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98 <td>
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99 If the user doesn't specify the value of a given parameter, the default value is used.
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100 </td>
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101 </tr>
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102 </table>
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103 </p>
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104 <h2>Algorithm</h2>
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105 <p>
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106 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
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107 compute the standard deviation of the mean for each frequency bin, the averaging of the different segments is performed using Welford's
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108 algorithm <a href="#references">[2]</a> which allows to compute mean and variance in one loop.
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109 </p>
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110 <h2>Examples</h2>
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111 <p>
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112 1. Evaluation of the PSD of a time-series represented by a low frequency sinewave signal, superimposed to
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113 white noise. Comparison of the effect of windowing on the estimate of the white noise level and
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114 on resolving the signal.
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115 </p>
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116 <div class="fragment"><pre>
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117 <br> <span class="comment">% create two AOs</span>
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118 x1 = 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>,1000,<span class="string">'fs'</span>,10));
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119 x2 = 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>,1000,<span class="string">'fs'</span>,10));
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120 <span class="comment">% add both AOs</span>
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121 x = x1 + x2;
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122 <span class="comment">% compute the psd changing the 'nfft'</span>
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123 y_lf = psd(x);
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124 y_hf = psd(x,plist(<span class="string">'nfft'</span>,1000));
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125 <span class="comment">% compare </span>
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126 iplot(y_lf, y_hf)
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127 </pre></div>
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128
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129 <img src="images/psd_1.png" alt="" border="3">
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130
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131 <p>
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132 2. Evaluation of the PSD of a time-series represented by a low frequency sinewave signal, superimposed to
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133 white noise and to a low frequency linear drift. In the example, the same spectrum is computed with different
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134 spectral windows.
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135 </p>
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136 <div class="fragment"><pre>
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137 <br> <span class="comment">% create three AOs</span>
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138 x1 = 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>,1000,<span class="string">'fs'</span>,10,<span class="string">'yunits'</span>,<span class="string">'m'</span>));
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139 x2 = 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>,1000,<span class="string">'fs'</span>,10,<span class="string">'yunits'</span>,<span class="string">'m'</span>));
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140 x3 = ao(plist(<span class="string">'tsfcn'</span>, <span class="string">'t.^2 + t'</span>,<span class="string">'nsecs'</span>,1000,<span class="string">'fs'</span>,10,<span class="string">'yunits'</span>,<span class="string">'m'</span>));
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141 <span class="comment">% add them</span>
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142 x = x1 + x2 + x3;
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143 <span class="comment">% compute psd with different windows</span>
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144 y_1 = psd(x,plist(<span class="string">'scale'</span>,<span class="string">'ASD'</span>,<span class="string">'order'</span>,1,<span class="string">'win'</span>,<span class="string">'BH92'</span>));
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145 y_2 = psd(x,plist(<span class="string">'scale'</span>,<span class="string">'ASD'</span>,<span class="string">'order'</span>,2,<span class="string">'win'</span>,<span class="string">'Hamming'</span>));
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146 y_3 = psd(x,plist(<span class="string">'scale'</span>,<span class="string">'ASD'</span>,<span class="string">'order'</span>,2,<span class="string">'win'</span>,<span class="string">'Kaiser'</span>,<span class="string">'psll'</span>,200));
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147 <span class="comment">% compare</span>
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148 iplot(y_1, y_2, y_3);
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149 </pre></div>
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150 <p>
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151 <img src="images/psd_2.png" alt="" border="3">
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152 </p>
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153 <h2><a name="references">References</a></h2>
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154
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155 <ol>
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156 <li> P.D. Welch, The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short,
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157 Modified Periodograms, <i>IEEE Trans. on Audio and Electroacoustics</i>, Vol. 15, No. 2 (1967), pp. 70 - 73.</a></li>
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158 <li> B. P. Weldford, Note on a Method for Calculating Corrected Sums of Squares and Products,
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159 <i>Technometrics<i>, Vol. 4, No. 3 (1962), pp 419 - 420.</li>
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160 </ol>
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161
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162
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163 </p>
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164
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165 <br>
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166 <br>
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167 <table class="nav" summary="Navigation aid" border="0" width=
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168 "100%" cellpadding="0" cellspacing="0">
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169 <tr valign="top">
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170 <td align="left" width="20"><a href="sigproc_methods.html"><img src=
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171 "b_prev.gif" border="0" align="bottom" alt=
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172 "Spectral Estimation Methods"></a>&nbsp;</td>
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173
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174 <td align="left">Spectral Estimation Methods</td>
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175
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176 <td>&nbsp;</td>
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177
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178 <td align="right">Cross-spectral density estimates</td>
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179
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180 <td align="right" width="20"><a href=
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181 "sigproc_cpsd.html"><img src="b_next.gif" border="0" align=
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182 "bottom" alt="Cross-spectral density estimates"></a></td>
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183 </tr>
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184 </table><br>
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185
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186 <p class="copy">&copy;LTP Team</p>
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187 </body>
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188 </html>