#ifndef _BAYES_FILTER_COVARIANCE#define _BAYES_FILTER_COVARIANCE/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Id: covFlt.hpp 562 2006-04-05 20:46:23 +0200 (Wed, 05 Apr 2006) mistevens $ *//* * Covariance Filter Scheme. *  Implemention of extended Kalman filter *  * To work with with Linear and Linrz models *  a) a state seperate from covariance predict is used. *  b) a EKF innovation update algorithm is used. * Discontinous observe models require that predict is normailised with * respect to the observation. * * A initial observation size may also be specified for efficiency. *  * The filter is operated by performing a * predict, observe * cycle defined by the base class */#include "bayesFlt.hpp"/* Filter namespace */namespace Bayesian_filter{class Covariance_scheme : public Extended_kalman_filter{public:	Covariance_scheme (std::size_t x_size, std::size_t z_initialsize = 0);	Covariance_scheme& operator= (const Covariance_scheme&);	// Optimise copy assignment to only copy filter state	void init ();	void update ();	Float predict (Linrz_predict_model& f);	// Standard Linrz prediction	Float predict (Gaussian_predict_model& f);	// Specialised 'stationary' prediction, only addative noise	Float observe_innovation (Linrz_uncorrelated_observe_model& h, const FM::Vec& s);	Float observe_innovation (Linrz_correlated_observe_model& h, const FM::Vec& s);public:						// Exposed Numerical Results	FM::SymMatrix S, SI;		// Innovation Covariance and Inverse	FM::Matrix W;				// Kalman Gainprotected:			   		// Permenantly allocated temps	FM::RowMatrix tempX;protected:					// allow fast operation if z_size remains constant	std::size_t last_z_size;	void observe_size (std::size_t z_size);};}//namespace#endif