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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: unsFlt.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>unsFlt.hpp</h1><a href="unsFlt_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><pre>00001 <span class="preprocessor">#ifndef _BAYES_FILTER_UNSCENTED</span>00002 <span class="preprocessor"></span><span class="preprocessor">#define _BAYES_FILTER_UNSCENTED</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/unsFlt.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"> * Unscented Filter Scheme.</span>00015 <span class="comment"> * A Julier-Uhlmann Unscented non-linear Kalman filter</span>00016 <span class="comment"> * Uses the classic implementation of Duplex Unscented transform.</span>00017 <span class="comment"> * The Unscented transform is used for non-linear state and observation predictions</span>00018 <span class="comment"> *</span>00019 <span class="comment"> * Observations are fused using innovation gain equations from a Covariance filter</span>00020 <span class="comment"> *</span>00021 <span class="comment"> * Predictions of state and state covariance (and observation) use</span>00022 <span class="comment"> * unscented transformations to interpolate the non-linear predict and observe</span>00023 <span class="comment"> * models. unscented transforms can be further optimised by vary the Kappa</span>00024 <span class="comment"> * parameter from its usual value of 1.</span>00025 <span class="comment"> * Discontinous observe models require that a normailisation function.</span>00026 <span class="comment"> *</span>00027 <span class="comment"> * The predict model is represtented by the state prediction function and a </span>00028 <span class="comment"> * seperate prediction noise matrix.</span>00029 <span class="comment"> * The observe model is represtented by the observation prediction function and</span>00030 <span class="comment"> * a function to normalise observeations.</span>00031 <span class="comment"> *</span>00032 <span class="comment"> * The filter is operated by performing a</span>00033 <span class="comment"> * predict, observe</span>00034 <span class="comment"> * cycle defined by the base class</span>00035 <span class="comment"> */</span>00036 <span class="preprocessor">#include "<a class="code" href="bayesFlt_8hpp.html">bayesFlt.hpp</a>"</span>00037 00038 <span class="comment">/* Filter namespace */</span>00039 <span class="keyword">namespace </span>Bayesian_filter00040 {00041 <a name="l00042"></a><a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html">00042</a> <span class="keyword">class </span><a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html">Unscented_predict_model</a> : <span class="keyword">public</span> <a class="code" href="classBayesian__filter_1_1Predict__model__base.html">Predict_model_base</a>00043 <span class="comment">/* Specific Unscented prediction model for Addative noise</span>00044 <span class="comment"> * x(k|k-1) = f(x(k-1|k-1)) + w(x(k))</span>00045 <span class="comment"> *</span>00046 <span class="comment"> * Unscented filter requires</span>00047 <span class="comment"> * f the function part of the non-linear model</span>00048 <span class="comment"> * Q the covariance of the addative w(x(k)), w is specificly allow to be a function of state</span>00049 <span class="comment"> */</span>00050 {00051 <span class="keyword">public</span>:<a name="l00052"></a><a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html#a0">00052</a> <a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html#a0">Unscented_predict_model</a> (size_t q_size)00053 {00054 q_unscented = q_size;00055 }00056 00057 <span class="keyword">virtual</span> <span class="keyword">const</span> FM::Vec& <a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html#a1">f</a>(<span class="keyword">const</span> FM::Vec& x) <span class="keyword">const</span> = 0;00058 <span class="comment">// Functional part of addative model</span>00059 <span class="comment">// Note: Reference return value as a speed optimisation, MUST be copied by caller.</span>00060 00061 <span class="keyword">virtual</span> <span class="keyword">const</span> FM::SymMatrix& <a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html#a2">Q</a>(<span class="keyword">const</span> FM::Vec& x) <span class="keyword">const</span> = 0;00062 <span class="comment">// Covariance of addative noise</span>00063 <span class="comment">// Note: Reference return value as a speed optimisation, MUST be copied by caller.</span>00064 <span class="keyword">private</span>:<a name="l00065"></a><a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html#n0">00065</a> <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classBayesian__filter_1_1Unscented__predict__model.html#n0">Unscented_filter</a>; <span class="comment">// Filter implementation need to know noise size</span>00066 size_t q_unscented;00067 };00068 00069 ⌨️ 快捷键说明
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