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Cleanup
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 18:04:34 +0100
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1 <p>
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2 Finite Impulse Response filters are those filters present a non-zero finite length response
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3 when excited with a very brief (ideally an infinite peak) input signal. A linear causal
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4 FIR filter can be described by the following difference equation
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5 </p>
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6 <div align="center">
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7 <IMG src="images/sigproc_8.png" width="157" height="56" align="middle" border="0">
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8 </div>
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9 <p>
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10 This operation describe a nonrecursive system, i.e. a system that only depends on current
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11 and past samples of the input data stream <tt>x[n]</tt>
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12 </p>
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13 <h2><a name="FIRbuild">Creating a FIR filter in the LTPDA</a></h2>
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14 <p>
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15 The LTPDA Toolbox allows the implementation of FIR filters by means of the
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16 <a href="class_desc_mfir.html"> mfir class</a>.
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17 </p>
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18 <h2><a name="FIRplist">Creating from a plist</a></h2>
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19 <p>
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20 The following example creates an order 64 highpass filter with high frequency gain 2.
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21 The filter is designed for 1 Hz sampled data and has a cut-off frequency of 0.2 Hz.
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22 </p>
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23 <div class="fragment"><pre>
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24
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25 pl = plist(<span class="string">'type'</span>, <span class="string">'highpass'</span>, ...
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26 <span class="string">'order'</span>, 64, ...
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27 <span class="string">'gain'</span>, 2.0, ...
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28 <span class="string">'fs'</span>, 1, ...
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29 <span class="string">'fc'</span>, 0.2);
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30 f = mfir(pl)
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31 </pre></div>
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32
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33 <h2><a name="FIRdiff">Creating from a difference equation</a></h2>
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34 <p>
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35 The filter can be defined in terms of two vectors specifying the coefficients of the filter
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36 and the sampling frequency. The following example creates a FIR filter with sampling frequency
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37 1 Hz and the following recursive equation:
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38 </p>
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39
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40 <div align="center">
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41 <IMG src="images/sigproc_10.png" width="202" height="28" align="middle" border="0"></div>
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42 </div>
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43
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44 <p><br></p>
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45
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46 <div class="fragment"><pre>
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47
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48 b = [-0.8 10];
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49 fs = 1;
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50 f = mfir(b,fs)
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51 </pre></div>
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52
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53 <h2><a name="FIRfromAO">Creating from an Analysis Object</a></h2>
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54 <p>
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55 A FIR filter can be generated based on the magnitude of the input Analysis Object or fsdata object.
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56 In the following example a fsdata object is first generated and then passed to the mfir constructor
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57 to obtain the equivalent FIR filter.
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58 </p>
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59
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60 <div class="fragment"><pre>
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61
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62 fs = 10; <span class="comment">% sampling frequency</span>
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63 f = linspace(0, fs/2, 1000);
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64 y = 1./(1+(0.1*2*pi*f).^2); <span class="comment">% an arbitrary function</span>
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65 fsd = fsdata(f,y,fs); <span class="comment">% build the fsdata object</span>
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66 f = mfir(ao(fsd));
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67
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68 </pre></div>
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69 <br>
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70 <p>
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71 Available methods for this option are: 'frequency-sampling' (uses fir2), 'least-squares' (uses firls)
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72 and 'Parks-McClellan' (uses firpm)
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73 </p>
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74 <h2><a name="IIRimport">Importing an existing model</a></h2>
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75 <p>
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76 The mfir constructor also accepts as an input existing models in different formats:
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77 </p>
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78 <li>
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79 <li><p>LISO files:<p>
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80 <div class="fragment"><pre>
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81 f = mfir(<span class="string">'foo_fir.fil'</span>)
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82 </pre></div>
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83 </li>
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84 <li><p>XML files:</p>
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85 <div class="fragment"><pre>
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86 f = mfir(<span class="string">'foo_fir.xml'</span>)
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87 </pre></div>
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88 <li><p>MAT files:</p>
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89 <div class="fragment"><pre>
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90 f = mfir(<span class="string">'foo_fir.mat'</span>)
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91 </pre></div>
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92 </li>
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93 <li><p>From repository:</p>
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94 <div class="fragment"><pre>
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95 f = mfir(plist(<span class="string">'hostname'</span>, <span class="string">'localhost'</span>, <span class="string">'database'</span>, <span class="string">'ltpda'</span>, <span class="string">'ID'</span>, []))
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96 </pre></div>
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97 </li>
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98 </ul>