root/bdm/estim/ekf_templ.h @ 254

/*!
  \file
  \brief Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/

#ifndef EKF_TEMP_H
#define EKF_TEMP_H

#include "libKF.h"

namespace bdm{

//!Extended Kalman filter with unknown \c Q and \c R
class EKFful_unQR : public EKFfull , public BMcond {
public:
        //! Default constructor
        EKFful_unQR ( RV rx, RV ry,RV ru,RV rQR ) :EKFfull ( rx,ry,ru ),BMcond ( rQR ) {};
        void condition ( const vec &QR0 ) {
                Q=diag(QR0(0,dimx-1));
                R=diag(QR0(dimx,dimx+dimy-1));
        };
};

//!Extended Kalman filter in Choleski form with unknown \c Q
class EKFCh_unQ : public EKFCh , public BMcond {
public:
        //! Default constructor
        EKFCh_unQ ( RV rx, RV ry,RV ru,RV rQ ) :EKFCh ( rx,ry,ru ),BMcond ( rQ ) {};
        void condition ( const vec &Q0 ) {
                Q.setD ( Q0,0 );
                //from EKF
                preA.set_submatrix ( dimy+dimx,dimy,Q._Ch() );
        };
};

//!Extended Kalman filter with unknown parameters in \c IM
class EKFCh_cond : public EKFCh , public BMcond {
        public:
        //! Default constructor
                EKFCh_cond ( RV rx, RV ry,RV ru,RV rC ) :EKFCh ( rx,ry,ru ),BMcond ( rC ) {};
                void condition ( const vec &val ) {
                        pfxu->condition ( val );
                };
};

}
#endif //EKF_TEMP_H