Mercurial > hg > ltpda
comparison m-toolbox/html_help/help/ug/sigproc_iir_content.html @ 0:f0afece42f48
Import.
| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 23 Nov 2011 19:22:13 +0100 |
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| 1 <p> | |
| 2 Infinite Impulse Response filters are those filters present a non-zero infinite length response when excited with a very brief (ideally an infinite peak) input signal. A linear causal IIR filter can be described by the following difference equation | |
| 3 </p> | |
| 4 <div align="center"> | |
| 5 <IMG src="images/sigproc_7.png" width="283" height="56" align="middle" border="0"> | |
| 6 </div> | |
| 7 <p> | |
| 8 This operation describe a recursive system, i.e. a system that depends on current and past samples of the input x[n], but also on the output data stream y[n]. | |
| 9 </p> | |
| 10 <h2><a name="IIRbuild">Creating a IIR filter in the LTPDA</a></h2> | |
| 11 | |
| 12 The LTPDA Toolbox allows the implementation of IIR filters by means of the <a href="pzmodel_filter.html"> miir class</a>. | |
| 13 | |
| 14 <h2><a name="IIRplist">Creating from a plist</a></h2> | |
| 15 <p> | |
| 16 The following example creates an order 1 highpass filter with high frequency gain 2. Filter is designed for 10 Hz sampled data and has a cut-off frequency of 0.2 Hz. | |
| 17 </p> | |
| 18 <div class="fragment"><pre> | |
| 19 | |
| 20 pl = plist(<span class="string">'type'</span>, <span class="string">'highpass'</span>, ... | |
| 21 <span class="string">'order'</span>, 1, ... | |
| 22 <span class="string">'gain'</span>, 2.0, ... | |
| 23 <span class="string">'fs'</span>, 10, ... | |
| 24 <span class="string">'fc'</span>, 0.2); | |
| 25 f = miir(pl) | |
| 26 </pre></div> | |
| 27 | |
| 28 <h2><a name="IIRpzmodel">Creating from a pzmodel</a></h2> | |
| 29 <p> | |
| 30 IIR filters can also be <a href="pzmodel_filter.html"> created from a pzmodel </a>. | |
| 31 </p> | |
| 32 <h2><a name="IIRdiff">Creating from a difference equation</a></h2> | |
| 33 <p> | |
| 34 Alternatively, the filter can be defined in terms of two vectors specifying the coefficients of the filter and the sampling frequency. The following example creates a IIR filter with sampling frequency 1 Hz and the following recursive equation: | |
| 35 </p> | |
| 36 | |
| 37 <div align="center"> | |
| 38 <IMG src="images/sigproc_9.png" width="299" height="28" align="middle" border="0"> | |
| 39 </div> | |
| 40 | |
| 41 <p><br></p> | |
| 42 | |
| 43 <div class="fragment"><pre> | |
| 44 | |
| 45 a = [0.5 -0.01]; | |
| 46 b = [1 0.1]; | |
| 47 fs = 1; | |
| 48 f = miir(a,b,fs) | |
| 49 </pre></div> | |
| 50 | |
| 51 <p> | |
| 52 <br> | |
| 53 Notice that the convetion used in this function is the one described in the <a href="sigproc_dfilt.html"> Digital filters classification</a> section | |
| 54 </p> | |
| 55 | |
| 56 <h2><a name="IIRimport">Importing an existing model</a></h2> | |
| 57 <p> | |
| 58 The miir constructor also accepts as an input existing models in different formats: | |
| 59 </p> | |
| 60 <li> | |
| 61 <li><p>LISO files:<p> | |
| 62 <div class="fragment"><pre> | |
| 63 f = miir(<span class="string">'foo_iir.fil'</span>) | |
| 64 </pre></div> | |
| 65 </li> | |
| 66 <li><p>XML files:</p> | |
| 67 <div class="fragment"><pre> | |
| 68 f = miir(<span class="string">'foo_iir.xml'</span>) | |
| 69 </pre></div> | |
| 70 <li><p>MAT files:</p> | |
| 71 <div class="fragment"><pre> | |
| 72 f = miir(<span class="string">'foo_iir.mat'</span>) | |
| 73 </pre></div> | |
| 74 </li> | |
| 75 <li><p>From repository:</p> | |
| 76 <div class="fragment"><pre> | |
| 77 f = miir(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>, [])) | |
| 78 </pre></div> | |
| 79 </li> | |
| 80 </ul> | |
| 81 |