#ifndef _BAYES_FILTER_INFORMATION#define _BAYES_FILTER_INFORMATION/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Id: infFlt.hpp 562 2006-04-05 20:46:23 +0200 (Wed, 05 Apr 2006) mistevens $ *//* * Information Filter Scheme. *  A possibly non-linear Information filter * * References * [1] "Stochastic Models, Estimation, and Control} Peter S Maybeck, Academic Press, ISBN 0-12-480701-1 *      Section 5.7 * [2] "Kalman Filtering, Theory and Practice", Mohinder S. Grewal, Angus P. Andrews ISBN 0-13-211335-X * To work with with Linear and Linrz models *  a) a seperate state and information (via covariance) predict is used. *  b) a EIF modified innovation update algorithm is used. * * Two alternative algorithms are used for predict functions: *  For linrz models an extended predict form is used so information state 'y' is predicted via the *  non-linear function. This requires that X, and Y are invertable so 'x' can be computed. * For linear invertable models predict can be done directly without computing x * Discontinous observe models require that predict is normailised with * respect to the observation. * NUMERICS *  The state x is respresented by prod(X,y). This may be illconditioned if the product is *  illconditioned. At present only the conditioning of X if checked, if y has a large range the product *  may be illconditioned. This is not checked * * The filter is operated by performing a *  predict, observe * cycle defined by the base class */#include "bayesFlt.hpp"/* Filter namespace */namespace Bayesian_filter{class Information_scheme : public Extended_kalman_filter, virtual public Information_state_filter{public:	Information_scheme (std::size_t x_size, std::size_t z_initialsize = 0);	Information_scheme& operator= (const Information_scheme&);	// Optimise copy assignment to only copy filter state	struct Linear_predict_byproducts	{		 Linear_predict_byproducts (std::size_t x_size, std::size_t q_size);		 FM::SymMatrix A;		 FM::Matrix tempG;		 FM::SymMatrix B;		 FM::Vec y;	};	void init ();	void update ();	void init_yY ();	void update_yY ();	// Covariance and information form state interface	Float predict (Linear_invertable_predict_model& f, Linear_predict_byproducts& b);	// Linear Prediction in information form as in Ref[2]	Float predict (Linear_invertable_predict_model& f)	{		Linear_predict_byproducts b(f.Fx.size1(),f.q.size());		return predict (f, b);		}	Float predict (Linrz_predict_model& f);	// Extended Prediction via state	Float observe_innovation (Linrz_uncorrelated_observe_model& h, const FM::Vec& s);	Float observe_innovation (Linrz_correlated_observe_model& h, const FM::Vec& s);protected:	bool update_required;	// Postcondition of update is not metprotected:			   		// Permenantly allocated temps	FM::RowMatrix tempX;	FM::Vec i;	FM::SymMatrix I;					// allow fast operation if z_size remains constant	FM::SymMatrix ZI;	std::size_t last_z_size;	void observe_size (std::size_t z_size);};}//namespace#endif