view m-toolbox/html_help/help/ug/sigproc_fir.html @ 0:f0afece42f48
Import.
author |
Daniele Nicolodi <nicolodi@science.unitn.it> |
date |
Wed, 23 Nov 2011 19:22:13 +0100 (2011-11-23) |
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<h1 class="title"><a name="f3-12899" id="f3-12899"></a>FIR Filters</h1>
<hr>
<p>
<p>
Finite Impulse Response filters are those filters present a non-zero finite length response
when excited with a very brief (ideally an infinite peak) input signal. A linear causal
FIR filter can be described by the following difference equation
</p>
<div align="center">
<IMG src="images/sigproc_8.png" width="157" height="56" align="middle" border="0">
</div>
<p>
This operation describe a nonrecursive system, i.e. a system that only depends on current
and past samples of the input data stream <tt>x[n]</tt>
</p>
<h2><a name="FIRbuild">Creating a FIR filter in the LTPDA</a></h2>
<p>
The LTPDA Toolbox allows the implementation of FIR filters by means of the
<a href="class_desc_mfir.html"> mfir class</a>.
</p>
<h2><a name="FIRplist">Creating from a plist</a></h2>
<p>
The following example creates an order 64 highpass filter with high frequency gain 2.
The filter is designed for 1 Hz sampled data and has a cut-off frequency of 0.2 Hz.
</p>
<div class="fragment"><pre>
pl = plist(<span class="string">'type'</span>, <span class="string">'highpass'</span>, ...
<span class="string">'order'</span>, 64, ...
<span class="string">'gain'</span>, 2.0, ...
<span class="string">'fs'</span>, 1, ...
<span class="string">'fc'</span>, 0.2);
f = mfir(pl)
</pre></div>
<h2><a name="FIRdiff">Creating from a difference equation</a></h2>
<p>
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 FIR filter with sampling frequency
1 Hz and the following recursive equation:
</p>
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<IMG src="images/sigproc_10.png" width="202" height="28" align="middle" border="0"></div>
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<p><br></p>
<div class="fragment"><pre>
b = [-0.8 10];
fs = 1;
f = mfir(b,fs)
</pre></div>
<h2><a name="FIRfromAO">Creating from an Analysis Object</a></h2>
<p>
A FIR filter can be generated based on the magnitude of the input Analysis Object or fsdata object.
In the following example a fsdata object is first generated and then passed to the mfir constructor
to obtain the equivalent FIR filter.
</p>
<div class="fragment"><pre>
fs = 10; <span class="comment">% sampling frequency</span>
f = linspace(0, fs/2, 1000);
y = 1./(1+(0.1*2*pi*f).^2); <span class="comment">% an arbitrary function</span>
fsd = fsdata(f,y,fs); <span class="comment">% build the fsdata object</span>
f = mfir(ao(fsd));
</pre></div>
<br>
<p>
Available methods for this option are: 'frequency-sampling' (uses fir2), 'least-squares' (uses firls)
and 'Parks-McClellan' (uses firpm)
</p>
<h2><a name="IIRimport">Importing an existing model</a></h2>
<p>
The mfir constructor also accepts as an input existing models in different formats:
</p>
<li>
<li><p>LISO files:<p>
<div class="fragment"><pre>
f = mfir(<span class="string">'foo_fir.fil'</span>)
</pre></div>
</li>
<li><p>XML files:</p>
<div class="fragment"><pre>
f = mfir(<span class="string">'foo_fir.xml'</span>)
</pre></div>
<li><p>MAT files:</p>
<div class="fragment"><pre>
f = mfir(<span class="string">'foo_fir.mat'</span>)
</pre></div>
</li>
<li><p>From repository:</p>
<div class="fragment"><pre>
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>, []))
</pre></div>
</li>
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