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Update ltpda_uo.update
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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1 <h2>Description</h2>
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2 <p>
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3 The LTPDA method <a href="matlab:doc('ao/lpsd')">ao/lpsd</a> estimates the power spectral density of time-series
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4 signals, included in the input <tt>ao</tt>s following the LPSD algorithm <a href="#references">[1]</a>. Spectral density estimates are not
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5 evaluated at frequencies which are linear multiples of the minimum frequency resolution <tt>1/T</tt>, where <tt>T</tt>
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6 is the window lenght, but on a logarithmic scale. The algorithm takes care of calculating the frequencies at which to evaluate
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7 the spectral estimate, aiming at minimizing the uncertainty in the estimate itself, and to recalculate a suitable
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8 window length for each frequency bin.
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9 </p>
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10 <p>
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11 Data are windowed prior to the estimation of the spectrum, by multiplying
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12 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
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13 of the border discontinuities. Detrending is performed on each individual window.
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14 The user can choose the quantity being given in output among ASD (amplitude spectral density),
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15 PSD (power spectral density), AS (amplitude spectrum), and PS (power spectrum).
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16 </p>
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17 <br>
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18 <h2>Syntax</h2>
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19 </p>
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20 <div class="fragment"><pre>
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21 <br> bs = lpsd(a1,a2,a3,...,pl)
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22 bs = lpsd(as,pl)
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23 bs = as.lpsd(pl)
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24 </pre>
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25 </div>
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26 <p>
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27 <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 parameter list.
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28
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29 <h2>Parameters</h2>
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30 <p>The parameter list <tt>pl</tt> includes the following parameters:</p>
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31 <ul>
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32 <li> <tt>'Kdes'</tt> - desired number of averages [default: 100]</li>
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33 <li> <tt>'Jdes'</tt> - number of spectral frequencies to compute [default: 1000]</li>
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34 <li> <tt>'Lmin'</tt> - minimum segment length [default: 0]</li>
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35 <li> <tt>'Win'</tt> - the window to be applied to the data to remove the
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36 discontinuities at edges of segments. [default: taken from user prefs].<br>
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37 The window is described by a string with its name and, only in the case of Kaiser window,
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38 the additional parameter <tt>'psll'</tt>. <br>For instance: plist('Win', 'Kaiser', 'psll', 200).
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39 </li>
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40 <li> <tt>'Olap'</tt> - segment percent overlap [default: -1, (taken from window function)] </li>
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41 <li> <tt>'Scale'</tt> - scaling of output. Choose from: <ul>
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42 <li> 'ASD' - amplitude spectral density </li>
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43 <li> 'PSD' - power spectral density [default] </li>
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44 <li> 'AS' - amplitude spectrum </li>
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45 <li> 'PS' - power spectrum </li> </ul> </li>
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46 <li> <tt>'Order'</tt> - order of segment detrending <ul>
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47 <li> -1 - no detrending </li>
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48 <li> 0 - subtract mean [default] </li>
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49 <li> 1 - subtract linear fit </li>
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50 <li> N - subtract fit of polynomial, order N </li> </ul> </li>
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51 </ul>
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52 The length of the window is set by the value of the parameter <tt>'Nfft'</tt>, so that the window
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53 is actually rebuilt using only the key features of the window, i.e. the name and, for Kaiser windows, the PSLL.
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54 </p>
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55
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56 <p>
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57 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
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58 <tr width="90%">
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59 <td>
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60 If the user doesn't specify the value of a given parameter, the default value is used.
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61 </td>
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62 </tr>
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63 </table>
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64 </p>
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65 <h2>Algorithm</h2>
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66 <p>
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67 The algorithm is implemented according to <a href="#references">[1]</a>. In order to
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68 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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69 algorithm <a href="#references">[2]</a> which allows to compute mean and variance in one loop. <br>
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70 In the LPSD algorithm, the first frequencies bins are usually computed using a single segment containing all the data.
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71 For these bins, the sample variance is set to <tt>Inf</tt>.
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72 </p>
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73 <h2>Examples</h2>
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74 <p>
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75 1. Evaluation of the ASD of a time-series represented by a low frequency sinewave signal, superimposed to
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76 white noise. Comparison of the effect of using standard Pwelch and LPSD on the estimate
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77 of the white noise level and on resolving the signal.
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78 </p>
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79 <div class="fragment"><pre>
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80 <br> <span class="comment">% Create input AO</span>
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81 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">'rad'</span>));
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82 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">'rad'</span>));
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83 x = x1 + x2;
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84
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85 <span class="comment">% Compute psd and lpsd </span>
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86 pl = 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">'Kaiser'</span>,<span class="string">'psll'</span>,200);
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87 y1 = psd(x, pl);
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88 y2 = lpsd(x, pl);
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89
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90 <span class="comment">% Compare</span>
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91 iplot(y1, y2)
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92 </pre>
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93 </div>
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94
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95 <img src="images/l_psd_1.png" border="3">
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96
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97
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98 <h2><a name="references">References</a></h2>
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99
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100 <ol>
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101 <li> M. Troebs, G. Heinzel, Improved spectrum estimation from digitized time series
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102 on a logarithmic frequency axis, <a href="http://dx.doi.org/10.1016/j.measurement.2005.10.010" ><i>Measurement</i>, Vol. 39 (2006), pp. 120 - 129</a>. See also the <a href="http://dx.doi.org/10.1016/j.measurement.2008.04.004" >Corrigendum</a>.</li>
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103 <li> B. P. Weldford, Note on a Method for Calculating Corrected Sums of Squares and Products,
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104 <i>Technometrics<i>, Vol. 4, No. 3 (1962), pp 419 - 420.</li>
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105 </ol>