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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"><html><head><meta http-equiv="Content-Type" content="text/html;charset=iso-8859-1"><title>Bayes++ Bayesian Filtering Classes: itrFlt.hpp Source File</title><link href="doxygen.css" rel="stylesheet" type="text/css"></head><body><!-- Generated by Doxygen 1.3.2 --><div class="qindex"><a class="qindex" href="index.html">Main Page</a> | <a class="qindex" href="namespaces.html">Namespace List</a> | <a class="qindex" href="hierarchy.html">Class Hierarchy</a> | <a class="qindex" href="classes.html">Alphabetical List</a> | <a class="qindex" href="annotated.html">Compound List</a> | <a class="qindex" href="files.html">File List</a> | <a class="qindex" href="namespacemembers.html">Namespace Members</a> | <a class="qindex" href="functions.html">Compound Members</a> | <a class="qindex" href="globals.html">File Members</a></div><h1>itrFlt.hpp</h1><a href="itrFlt_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><pre>00001 <span class="preprocessor">#ifndef _BAYES_FILTER_ITERATED_COVARIANCE</span>00002 <span class="preprocessor"></span><span class="preprocessor">#define _BAYES_FILTER_ITERATED_COVARIANCE</span>00003 <span class="preprocessor"></span>00004 <span class="comment">/*</span>00005 <span class="comment"> * Bayes++ the Bayesian Filtering Library</span>00006 <span class="comment"> * Copyright (c) 2002 Michael Stevens</span>00007 <span class="comment"> * See accompanying Bayes++.htm for terms and conditions of use.</span>00008 <span class="comment"> *</span>00009 <span class="comment"> * $Header: /cvsroot/bayesclasses/Bayes++/BayesFilter/itrFlt.hpp,v 1.7.2.1 2004/02/11 21:06:42 mistevens Exp $</span>00010 <span class="comment"> * $NoKeywords: $</span>00011 <span class="comment"> */</span>00012 00013 <span class="comment">/*</span>00014 <span class="comment"> * Iterated Covariance Filter Scheme.</span>00015 <span class="comment"> * A non-linear Covariance (Kalman) filter with relinearisation and iteration</span>00016 <span class="comment"> *</span>00017 <span class="comment"> * The observe algorithm uses the iterated non-linear formulation </span>00018 <span class="comment"> * from Bar-Shalom and Fortmann p.119 (full scheme)</span>00019 <span class="comment"> * Discontinous observe models require that state is normailised with</span>00020 <span class="comment"> * respect to the observation.</span>00021 <span class="comment"> *</span>00022 <span class="comment"> * The filter is operated by performing a</span>00023 <span class="comment"> * predict, observe</span>00024 <span class="comment"> * cycle defined by the base class</span>00025 <span class="comment"> */</span>00026 <span class="preprocessor">#include "<a class="code" href="bayesFlt_8hpp.html">bayesFlt.hpp</a>"</span>00027 00028 <span class="comment">/* Filter namespace */</span>00029 <span class="keyword">namespace </span>Bayesian_filter00030 {00031 00032 <span class="keyword">class </span>Iterated_covariance_scheme;00033 <a name="l00034"></a><a class="code" href="classBayesian__filter_1_1Iterated__observe__model.html">00034</a> <span class="keyword">class </span><a class="code" href="classBayesian__filter_1_1Iterated__observe__model.html">Iterated_observe_model</a> : <span class="keyword">virtual</span> <span class="keyword">public</span> <a class="code" href="classBayesian__filter_1_1Observe__model__base.html">Observe_model_base</a>00035 <span class="comment">/* Linrz observation model which can be iterated</span>00036 <span class="comment"> Hx can be relinearised</span>00037 <span class="comment"> */</span>00038 {00039 <span class="keyword">protected</span>: <span class="comment">// model is not sufficient, it is used to build observe model's</span><a name="l00040"></a><a class="code" href="classBayesian__filter_1_1Iterated__observe__model.html#b0">00040</a> <a class="code" href="classBayesian__filter_1_1Iterated__observe__model.html#b0">Iterated_observe_model</a> ()00041 {}00042 <span class="keyword">public</span>:00043 <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBayesian__filter_1_1Iterated__observe__model.html#a0">relinearise</a> (<span class="keyword">const</span> FM::Vec& x) =0;00044 <span class="comment">// Relinearised about state x</span>00045 };00046 00047 <a name="l00048"></a><a class="code" href="classBayesian__filter_1_1Iterated__terminator.html">00048</a> <span class="keyword">class </span><a class="code" href="classBayesian__filter_1_1Iterated__terminator.html">Iterated_terminator</a> : <span class="keyword">public</span> <a class="code" href="classBayesian__filter_1_1Bayes__base.html">Bayes_base</a>00049 <span class="comment">/*</span>00050 <span class="comment"> * Termination condition for filter Iteration</span>00051 <span class="comment"> * Used by iterated observe to parameterise termination condition</span>00052 <span class="comment"> * If iteration continues, the terminator must also relinearise the model about the filters new state</span>00053 <span class="comment"> *</span>00054 <span class="comment"> * Defaults to immediately terminating the iteration</span>00055 <span class="comment"> *</span>00056 <span class="comment"> * A more useful terminator can built by derivation.</span>00057 <span class="comment"> * For example terminator constructed with a reference to the filter and model can</span>00058 <span class="comment"> * detect convergence of x and/or X</span>00059 <span class="comment"> */</span>00060 {00061 <span class="keyword">public</span>:<a name="l00062"></a><a class="code" href="classBayesian__filter_1_1Iterated__terminator.html#a0">00062</a> <span class="keyword">virtual</span> <span class="keywordtype">bool</span> <a class="code" href="classBayesian__filter_1_1Iterated__terminator.html#a0">term_or_relinearize</a> (<span class="keyword">const</span> <a class="code" href="classBayesian__filter_1_1Iterated__covariance__scheme.html">Iterated_covariance_scheme</a>& f)00063 {00064 <span class="keywordflow">return</span> <span class="keyword">true</span>;⌨️ 快捷键说明
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