/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Id: simpleExample.cpp 564 2006-04-05 20:51:38 +0200 (Wed, 05 Apr 2006) mistevens $ *//* * Example of using Bayesian Filter Class to solve a simple problem. *  A linear filter with one state and constant noises */// Use the Unscented filter scheme from Bayes++#include "BayesFilter/unsFlt.hpp"#include <iostream>#include <boost/numeric/ublas/io.hpp>using namespace std;using namespace Bayesian_filter;using namespace Bayesian_filter_matrix;/* * Simple Prediction model */class Simple_predict : public Linear_predict_model{public:	Simple_predict() : Linear_predict_model(1,1)	// Construct a constant model	{				// Stationary Prediction model (Identity)		Fx(0,0) = 1.;				// Constant Noise model with a large variance		q[0] = 2.;		G(0,0) = 1.;	}};/* * Simple Observation model */class Simple_observe : public Linear_uncorrelated_observe_model{public:	Simple_observe () : Linear_uncorrelated_observe_model(1,1)	// Construct a constant model	{				// Linear model		Hx(0,0) = 1;				// Constant Observation Noise model with variance of one		Zv[0] = 1.;	}};/* * Filter a simple example */int main(){	// Global setup for test output	cout.flags(ios::fixed); cout.precision(4);	// Construct simple Prediction and Observation models	Simple_predict my_predict;	Simple_observe my_observe;	// Use an 'unscented' filter scheme with one state	Unscented_scheme my_filter(1);	// Setup the initial state and covariance	Vec x_init (1); SymMatrix X_init (1, 1);	x_init[0] = 10.;		// Start at 10 with no uncertainty	X_init(0,0) = 0.;	my_filter.init_kalman (x_init, X_init);	cout << "Initial  " << my_filter.x << my_filter.X << endl;	// Predict the filter forward	my_filter.predict (my_predict);	my_filter.update();		// Update the filter, so state and covariance are available	cout << "Predict  " << my_filter.x << my_filter.X << endl;	// Make an observation	Vec z(1);	z[0] = 11.;				// Observe that we should be at 11	my_filter.observe (my_observe, z);	my_filter.update();		// Update the filter to state and covariance are available	cout << "Filtered " << my_filter.x << my_filter.X << endl;	return 0;}