EKF< sq_T > Class Template Reference

Extended Kalman Filter. More...

#include <libKF.h>

Inheritance diagram for EKF< sq_T >:

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Collaboration diagram for EKF< sq_T >:

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List of all members.

Public Member Functions
	EKF (RV rvx, RV rvy, RV rvu)
	Default constructor.
void	set_parameters (diffbifn *pfxu, diffbifn *phxu, const sq_T Q0, const sq_T R0)
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
void	set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const ldmat &R0, const ldmat &Q0)
	Set parameters with check of relevance.
void	set_est (const vec &mu0, const ldmat &P0)
	Set estimate values, used e.g. in initialization.
void	bayes (mat Dt)
	Batch Bayes rule (columns of Dt are observations).
epdf &	_epdf ()
	Returns a pointer to the epdf representing posterior density on parameters. Use with care!
Protected Attributes
RV	rvy
RV	rvu
int	dimx
int	dimy
int	dimu
mat	A
mat	B
mat	C
mat	D
ldmat	R
ldmat	Q
enorm< ldmat >	est
	posterior density on $x_t$
enorm< ldmat >	fy
	preditive density on $y_t$
mat	_K
vec *	_yp
ldmat *	_Ry
ldmat *	_iRy
vec *	_mu
ldmat *	_P
ldmat *	_iP
RV	rv
	Random variable of the posterior.
double	ll
	Logarithm of marginalized data likelihood.
bool	evalll
	If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save time.

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Detailed Description

template<class sq_T>
class EKF< sq_T >

Extended Kalman Filter.

An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean.

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The documentation for this class was generated from the following file:

• work/mixpp/bdm/estim/libKF.h

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