EKF< sq_T > Class Template Reference

Extended Kalman Filter. More...

#include <libKF.h>

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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)
 Set nonlinear functions for mean values and covariance matrices.
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 fsqmat &R0, const fsqmat &Q0)
 Set parameters with check of relevance.
void set_est (const vec &mu0, const fsqmat &P0)
 Set estimate values, used e.g. in initialization.
void bayes (mat Dt)
 Batch Bayes rule (columns of Dt are observations).
epdf_epdf ()
 access function
mat & __K ()
 access function
vec _dP ()
 access function
const RV_rv () const
 access function
double _ll () const
 access function

Protected Attributes

RV rvy
 Indetifier of output rv.
RV rvu
 Indetifier of exogeneous rv.
int dimx
 cache of rv.count()
int dimy
 cache of rvy.count()
int dimu
 cache of rvu.count()
mat A
 Matrix A.
mat B
 Matrix B.
mat C
 Matrix C.
mat D
 Matrix D.
fsqmat Q
 Matrix Q in square-root form.
fsqmat R
 Matrix R in square-root form.
enorm< fsqmatest
 posterior density on $x_t$
enorm< fsqmatfy
 preditive density on $y_t$
mat _K
 placeholder for Kalman gain
vec & _yp
 cache of fy.mu
fsqmat_Ry
 cache of fy.R
vec & _mu
 cache of est.mu
fsqmat_P
 cache of est.R
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.


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.


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

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