Mercurial > hg > ltpda

comparison m-toolbox/html_help/help/ug/sigproc_cpsd_content.html @ 0:f0afece42f48

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
parents
children
comparison
equal deleted inserted replaced
-1:000000000000 0:f0afece42f48
1 <h2>Description</h2>
2 <p>
3 Cross-power spectral density is performed by the Welch's averaged, modified periodogram method.
4 The LTPDA method <a href="matlab:doc('ao/cpsd')">ao/cpsd</a> estimates the cross-spectral density of time-series
5 signals, included in the input <tt>ao</tt>s following the Welch's averaged, modified periodogram method <a href="#references">[1]</a>.
6 Data are windowed prior to the estimation of the spectra, by multiplying
7 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
8 of the border discontinuities. The window length is adjustable to shorter lenghts to reduce the spectral
9 density uncertainties, and the percentage of subsequent window overlap can be adjusted as well.
10 <br>
11 <br>
12 <h2>Syntax</h2>
13 </p>
14 <div class="fragment"><pre>
15 <br> b = cpsd(a1,a2,pl)
16 </pre>
17 </div>
18 <p>
19 <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,
20 and <tt>pl</tt> is an optional parameters list.
21 <h2>Parameters</h2>
22 The parameter list <tt>pl</tt> includes the following parameters:</p>
23 <ul>
24 <li> <tt>'Nfft'</tt> - number of samples in each fft [default: length of input data]
25 A string value containing the variable 'fs' can
26 also be used, e.g., plist('Nfft', '2*fs') </li>
27 <li> <tt>'Win'</tt> - the window to be applied to the data to remove the
28 discontinuities at edges of segments. [default: taken from user prefs].<br>
29 The window is described by a string with its name and, only in the case of Kaiser window,
30 the additional parameter <tt>'psll'</tt>. <br>For instance: plist('Win', 'Kaiser', 'psll', 200). </li>
31 <li> <tt>'Olap'</tt> - segment percent overlap [default: -1, (taken from window function)] </li>
32 <li> <tt>'Order'</tt> - order of segment detrending <ul>
33 <li> -1 - no detrending </li>
34 <li> 0 - subtract mean [default] </li>
35 <li> 1 - subtract linear fit </li>
36 <li> N - subtract fit of polynomial, order N </li> </ul> </li>
37 <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>
38 <li><tt>'Times'</tt> - interval of time to evaluate the calculation on. If empty [default], it will take the whole section.</li>
39 </ul>
40 <p>
41 The length of the window is set by the value of the parameter <tt>'Nfft'</tt>, so that the window
42 is actually rebuilt using only the key features of the window, i.e. the name and, for Kaiser windows, the PSLL.
43 </p>
44
45 <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>
46 <p>
47 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
48 <tr width="90%">
49 <td>
50 If the user doesn't specify the value of a given parameter, the default value is used.
51 </td>
52 </tr>
53 </table>
54 </p>
55
56 <p>
57 The function makes CPSD estimates between the 2 input <tt>ao</tt>s. The input argument
58 list must contain 2 analysis objects, and the output will contain the CPSD estimate.
59 If passing two identical objects <tt>ai</tt>, the output will be equivalent to the output of <tt>psd(ai)</tt>.
60 </p>
61 </pre> </div>
62 </p>
63 <p>
64 <h2>Algorithm</h2>
65 <p>
66 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
67 compute the standard deviation of mean for each frequency bin, the averaging of the different segments is performed using Welford's
68 algorithm <a href="#references">[2]</a> which allows to compute mean and variance in one loop.
69 </p>
70 <h2>Example</h2>
71 </p>
72 <p>
73 Evaluation of the CPSD of two time-series represented by: a low frequency sinewave signal superimposed to
74 white noise, and a low frequency sinewave signal at the same frequency, phase shifted and with different
75 amplitude, superimposed to white noise.
76 </p>
77 <div class="fragment"><pre>
78 nsecs = 1000;
79 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)) + ...
80 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));
81 x.setYunits(<span class="string">'m'</span>);
82 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)) + ...
83 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));
84 y.setYunits(<span class="string">'V'</span>);
85 z = cpsd(x,y,plist(<span class="string">'nfft'</span>,1000));
86 iplot(z);
87 </pre>
88 </div>
89
90 <img src="images/cpsd_1.png" alt="" border="3">
91 <br>
92
93 <h2><a name="references">References</a></h2>
94
95 <ol>
96 <li> P.D. Welch, The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short,
97 Modified Periodograms, <i>IEEE Trans. on Audio and Electroacoustics</i>, Vol. 15, No. 2 (1967), pp. 70 - 73</a></li>
98 <li> B. P. Weldford, Note on a Method for Calculating Corrected Sums of Squares and Products,
99 <i>Technometrics<i>, Vol. 4, No. 3 (1962), pp 419 - 420.</li>
100 </ol>
101
102
103
104