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 (diffbifn *pfxu, diffbifn *phxu, sq_T Q0, sq_T R0, vec mu0, mat P0)
	Default constructor.
void	bayes (const vec &dt, bool evalll=true)
	Here dt = [yt;ut] of appropriate dimensions.
virtual void	bayes (const vec &dt)=0
	Incremental Bayes rule.
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!
Public Attributes
vec	mu
	Mean value of the posterior density.
fsqmat	P
	Mean value of the posterior density.
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.
Protected Attributes
int	dimx
int	dimy
int	dimu
mat	A
mat	B
mat	C
mat	D
fsqmat	R
fsqmat	Q
mat	_K
vec	_yp
fsqmat	_Ry
fsqmat	_iRy
Friends
std::ostream &	operator<< (std::ostream &os, const KalmanFull &kf)

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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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Member Function Documentation

virtual void BM::bayes

(

const vec &

dt

)

[pure virtual, inherited]

Incremental Bayes rule.

Parameters:

dt

vector of input data

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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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