#ifndef _BAYES_FILTER_INDIRECT#define _BAYES_FILTER_INDIRECT/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/BayesFilter/filters/indirect.hpp,v 1.6.2.1 2004/02/11 21:06:42 mistevens Exp $ * $NoKeywords: $ */ /* * Indirect Filter Adaptor *  Hides the details of the indirect operation of the filter *  The error filter uses the same linear models as the direct filter, *  observation error computation (subtraction) is linear! *//* Filter namespace */namespace Bayesian_filter{template <typename Error_base>class Indirect_state_filter : public State_filter {/* * Indirect state filter *  Estimates state using an associated observation error filter */public:	Indirect_state_filter (Error_base& error_filter)		: State_filter(error_filter.x.size()), direct(error_filter)	{	// Construct and zero initial error filter		direct.x.clear();	}	template <typename P_model>	void predict (P_model& f)	{		x = f.f(x);		direct.predict(f);				// May be optimised for linear f as x = 0	};	template <typename O_model>	void observe (O_model& h, const FM::Vec& z)	{				// Observe error (explict temporary)		FM::Vec z_error(z.size());		z_error = h.h(x);		z_error -= z;		direct.observe (h, z_error);				// Update State estimate with error		x -= direct.x;				// Reset the error		direct.x.clear();	}	template <typename O_model>	void observe_error (O_model& h, const FM::Vec& z_error)	{		direct.observe (h, z_error);				// Update State estimate with error		x -= direct.x;				// Reset the error		direct.x.clear();	}	void update ()	/* Update filters state	     Updates x(k|k)	*/	{}private:	Error_base& direct;};template <typename Error_base>class Indirect_kalman_filter : public Kalman_state_filter {/* * Indirect kalman filter *  Estimates state using an associated observation error filter */public:	Indirect_kalman_filter (Error_base& error_filter)		: Kalman_state_filter(error_filter.x.size()), direct(error_filter)	{		}	void init ()	/* Initialise from state and state covariance	*/	{		direct.x.clear();				// Zero initial error		direct.X = X;		direct.init();	}	template <typename P_model>	void predict (P_model& f)	{		x = f.f(x);		direct.predict(f);				// May be optimised for linear f as x = 0	};	template <typename O_model>	void observe (O_model& h, const FM::Vec& z)	{				// Observe error (explict temporary)		FM::Vec z_error(z.size());		z_error = h.h(x);		z_error -= z;		direct.observe (h, z_error);		direct.update();				// Update State estimate with error		x -= direct.x;				// Reset the error		direct.x.clear();		direct.init ();	}	template <typename O_model>	void observe_error (O_model& h, const FM::Vec& z_error)	{		direct.observe (h, z_error);				// Update State estimate with error		x -= direct.x;				// Reset the error		direct.clear();	}	void update ()	/* Update filters state	     Updates x(k|k)	*/	{		direct.update();		X = direct.X;	}private:	Error_base& direct;};}//namespace#endif