/* * Bayes++ the Bayesian Filtering Library * See Bayes++.htm for copyright license details * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/BayesFilter/covFlt.cpp,v 1.19.2.4 2005/01/15 11:24:33 mistevens Exp $ * $NoKeywords: $ *//* * Covariance Filter. */#include "covFlt.hpp"#include "matSup.hpp"/* Filter namespace */namespace Bayesian_filter{	using namespace Bayesian_filter_matrix;Covariance_scheme::Covariance_scheme (std::size_t x_size, std::size_t z_initialsize) :	Kalman_state_filter(x_size),	S(Empty), SI(Empty), W(Empty),	tempX(x_size,x_size)/* * Initialise filter and set the size of things we know about */{	last_z_size = 0;	// Leave z_size dependants Empty if z_initialsize==0	observe_size (z_initialsize);}Covariance_scheme& Covariance_scheme::operator= (const Covariance_scheme& a)/* Optimise copy assignment to only copy filter state * Precond: matrix size conformance */{	Kalman_state_filter::operator=(a);	return *this;}void Covariance_scheme::init (){						// Postconditions	if (!isPSD (X))		error (Numeric_exception("Initial X not PSD"));}void Covariance_scheme::update (){	// Nothing to do, implicit in observation}Bayes_base::Float Covariance_scheme::predict (Linrz_predict_model& f)/* Standard Linrz prediction */{	x = f.f(x);		// Extended Kalman state predict is f(x) directly						// Predict state covariance	noalias(X) = prod_SPD(f.Fx,X, tempX);	noalias(X) += prod_SPD(f.G, f.q, tempX);	return 1;}Bayes_base::Float Covariance_scheme::predict (Gaussian_predict_model& f)/* Specialised 'stationary' predict, only addative noise */{						// Predict state covariance, simply add in noise	noalias(X) += prod_SPD(f.G, f.q, tempX);  	return 1;}void Covariance_scheme::observe_size (std::size_t z_size)/* * Optimised dynamic observe sizing */{	if (z_size != last_z_size) {		last_z_size = z_size;		S.resize(z_size,z_size, false);		SI.resize(z_size,z_size, false);		W.resize(x.size(),z_size, false);	}}Bayes_base::Float Covariance_scheme::observe_innovation (Linrz_correlated_observe_model& h, const FM::Vec& s)/* correlated innovation observe */{						// Size consistency, z to model	if (s.size() != h.Z.size1())		error (Logic_exception("observation and model size inconsistent"));	observe_size (s.size());// Dynamic sizing						// Innovation covariance	Matrix temp_XZ (prod(X, trans(h.Hx)));	noalias(S) = prod(h.Hx, temp_XZ) + h.Z;						// Inverse innovation covariance	Float rcond = UdUinversePD (SI, S);	rclimit.check_PD(rcond, "S not PD in observe");						// Kalman gain, X*Hx'*SI	noalias(W) = prod(temp_XZ, SI);						// State update	noalias(x) += prod(W, s);	noalias(X) -= prod_SPD(W, S, temp_XZ);	return rcond;}Bayes_base::Float Covariance_scheme::observe_innovation (Linrz_uncorrelated_observe_model& h, const FM::Vec& s)/* uncorrelated innovation observe */{						// Size consistency, z to model	if (s.size() != h.Zv.size())		error (Logic_exception("observation and model size inconsistent"));	observe_size (s.size());// Dynamic sizing						// Innovation covariance	Matrix temp_XZ (prod(X, trans(h.Hx)));	noalias(S) = prod(h.Hx, temp_XZ);	for (std::size_t i = 0; i < h.Zv.size(); ++i)		S(i,i) += Float(h.Zv[i]);	// ISSUE mixed type proxy assignment						// Inverse innovation covariance	Float rcond = UdUinversePD (SI, S);	rclimit.check_PD(rcond, "S not PD in observe");						// Kalman gain, X*Hx'*SI	noalias(W) = prod(temp_XZ, SI);						// State update	noalias(x) += prod(W, s);	noalias(X) -= prod_SPD(W, S, temp_XZ);	return rcond;}}//namespace