/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/QuadCalib/QuadCalib.cpp,v 1.11.2.1 2004/02/11 21:37:23 mistevens Exp $ *//* * Example of using Bayesian Filter Class to solve a simple problem. * * The example implements a simple quadratic observer. *  This trys to estimate the state of system while also trying to *  calibrate a simple linear model of the system which includes *  a scale factor and a bias. *  Estimating both the system state and a scale factor results in a *  quadtratic (product of two states and therefore non-linear) observation. *  The system model is a 1D brownian motion with a known pertubation. */#include "BayesFilter/infFlt.hpp"#include "Test/random.hpp"#include <cmath>#include <iostream>#include <boost/numeric/ublas/io.hpp>#include <boost/random.hpp>namespace{	namespace FM = Bayesian_filter_matrix;	using namespace FM;	// Choose Filtering Scheme to use	typedef Bayesian_filter::Information_scheme FilterScheme;	// Square 	template <class scalar>	inline scalar sqr(scalar x)	{		return x*x;	}	// Random numbers from Boost	Bayesian_filter_test::Boost_random localRng;	// Constant Dimensions	const unsigned NX = 3;			// Filter State dimension 	(SystemState, Scale, Bias)	// Filter Parameters	// Noise on observing system state	const Float OBS_NOISE = 0.01;	// Prediction Noise: no Prediction noise as pertubation is known	const Float X_NOISE = 0.0;	// System State		const Float S_NOISE = 0.0;	// Scale	const Float B_NOISE = 0.0;	// Bias	// Filter's Initial state uncertainty: System state is unknown	const Float i_X_NOISE = 1000.;	const Float i_S_NOISE = 0.1;	const Float i_B_NOISE = 0.1;}//namespace/* * Prediction model * Linear state predict model with additive control input */class QCpredict : public Bayesian_filter::Linrz_predict_model{	Float motion;	mutable FM::Vec fx;public:	QCpredict();		;	void predict(const FM::Vec& u)	{		motion = u[0];	}	const FM::Vec& f(const FM::Vec& x) const	{		// Constant scale and bias, system state pertubed by control input		fx = x;		fx[0] += motion;		return fx;	};};QCpredict::QCpredict() : Bayesian_filter::Linrz_predict_model(NX, NX), fx(NX){	FM::identity (Fx);	// Setup constant noise model: G is identity	q[0] = sqr(X_NOISE);	q[1] = sqr(S_NOISE);	q[2] = sqr(B_NOISE);	FM::identity (G);}/* * Quadratic observation model */class QCobserve : public Bayesian_filter::Linrz_uncorrelated_observe_model{	mutable FM::Vec z_pred;public:	QCobserve ();	const FM::Vec& h(const FM::Vec& x) const	{	// Quadratic Observation model		z_pred[0] = x[0] * x[1] + x[2];		return z_pred;	};	void state (const FM::Vec& x)	// Linearised model, Jacobian of h at x	{		Hx(0,0) = x[1];		Hx(0,1) = x[0];		Hx(0,2) = 1.;	}};QCobserve::QCobserve () :	Bayesian_filter::Linrz_uncorrelated_observe_model(NX,1), z_pred(1){	// Observation Noise variance	Zv[0] = OBS_NOISE*OBS_NOISE;}int main(){	// Global setup for test output	std::cout.flags(std::ios::scientific); std::cout.precision(6);	// Setup the test filters	FM::Vec x_true (NX);	// True State to be observed	x_true[0] = 10.;	// System State	x_true[1] = 1.0;	// Scale	x_true[2] = 0.0;	// Bias	std::cout << "Quadratic Calibration" << std::endl;	std::cout << "Init " << x_true << std::endl;	// Construct Prediction and Observation model and Calibration filter	QCpredict linearPredict;	QCobserve nonlinObserve;	FilterScheme obsAndCalib (NX);	// Give the filter an true initial guess of the system state	obsAndCalib.x[0] = x_true[0];	obsAndCalib.x[1] = 1.;		// Assumed initial Scale	obsAndCalib.x[2] = 0.;		// Assumed initial Bias	obsAndCalib.X.clear();	obsAndCalib.X(0,0) = sqr(i_X_NOISE);	obsAndCalib.X(1,1) = sqr(i_S_NOISE);	obsAndCalib.X(2,2) = sqr(i_B_NOISE);	obsAndCalib.init ();	// Iterate the filter with test observations	FM::Vec u(1), z_true(1), z(1);	for (unsigned i = 0; i < 100; i++ )	{		// Predict true state using Brownian control input 		localRng.normal (u);				// normally distributed		x_true[0] += u[0];		linearPredict.predict (u);		// Predict filter with known pertubation		obsAndCalib.predict (linearPredict);		// True Observation: Quadratic observation model		z_true[0] = x_true[0] * x_true[1] + x_true[2];		// Observation with addative noise		localRng.normal (z, z_true[0], OBS_NOISE);	// normally distributed mean z_true[0], stdDev OBS_NOISE.		// Filter observation using model linearised at state estimate x		nonlinObserve.state (obsAndCalib.x);		obsAndCalib.observe (nonlinObserve, z);	}	// Update the filter to state and covariance are available	obsAndCalib.update ();	// Print everything: True, filter, covariance	std::cout << "True " << x_true <<  std::endl;	std::cout << "Calb " << obsAndCalib.x << std::endl;	std::cout << obsAndCalib.X << std::endl;	return 0;}