EKFfull KalmanFull BM EKF_unQful EKFful_unQR libKF.h diffbifn * diffbifn* EKFfull::pfxu pfxu Internal Model f(x,u). diffbifn * diffbifn* EKFfull::phxu phxu Observation Model h(x,u). enorm< fsqmat > enorm<fsqmat> EKFfull::E E EKFfull::EKFfull (RV rvx, RV rvy, RV rvu) EKFfull RV rvx RV rvy RV rvu Default constructor. void void EKFfull::set_parameters (diffbifn *pfxu, diffbifn *phxu, const mat Q0, const mat R0) set_parameters diffbifn * pfxu diffbifn * phxu const mat Q0 const mat R0 Set nonlinear functions for mean values and covariance matrices. diffbifn::_dimu fnc::_dimy RV::count diffbifn::dfdx_cond KalmanFull::mu BM::rv main void void EKFfull::bayes (const vec &dt) bayes bayes const vec & dt Here dt = [yt;ut] of appropriate dimensions. diffbifn::dfdx_cond diffbifn::eval BM::evalll BM::ll KalmanFull::mu KalmanFull::P enorm< sq_T >::set_mu main void void EKFfull::set_est (vec mu0, mat P0) set_est vec mu0 mat P0 set estimates KalmanFull::mu KalmanFull::P main epdf & epdf& EKFfull::_epdf () _epdf _epdf dummy! main void void BM::bayes (mat Dt) bayes mat Dt Batch Bayes rule (columns of Dt are observations). const RV & const RV& BM::_rv () const _rv access function BM::rv double double BM::_ll () const _ll access function BM::ll int int KalmanFull::dimx dimx int int KalmanFull::dimy dimy int int KalmanFull::dimu dimu mat mat KalmanFull::A A mat mat KalmanFull::B B mat mat KalmanFull::C C mat mat KalmanFull::D D mat mat KalmanFull::R R mat mat KalmanFull::Q Q mat mat KalmanFull::_Pp _Pp mat mat KalmanFull::_Ry _Ry mat mat KalmanFull::_iRy _iRy mat mat KalmanFull::_K _K RV RV BM::rv rv Random variable of the posterior. BM::_rv MPF< BM_T >::MPF set_parameters ARX::structure_est double double BM::ll ll Logarithm of marginalized data likelihood. BM::_ll EKFfixed::bayes EKF< sq_T >::bayes Kalman< sq_T >::bayes EKFCh::bayes KalmanCh::bayes bayes ARX::bayes bool bool BM::evalll 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. EKFfixed::bayes EKF< sq_T >::bayes Kalman< sq_T >::bayes EKFCh::bayes KalmanCh::bayes bayes ARX::bayes vec vec KalmanFull::mu mu Mean value of the posterior density. bayes KalmanFull::bayes KalmanFull::KalmanFull set_est set_parameters mat mat KalmanFull::P P Variance of the posterior density. bayes KalmanFull::bayes KalmanFull::KalmanFull set_est bool bool KalmanFull::evalll evalll double double KalmanFull::ll ll friend std::ostream & std::ostream& operator<< (std::ostream &os, const KalmanFull &kf) operator<< std::ostream & os const KalmanFull & kf print elements of KF Extended Kalman Filter in full matrices. An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. BM EKF_unQful EKFful_unQR EKFfull KalmanFull enorm< sq_T > R sq_T epdf rv BM rv fsqmat enorm< fsqmat > R < fsqmat > RV sqmat diffbifn rvu rvx fnc eEF EKFfull pfxu phxu E KalmanFull EKFfull_epdf EKFfull_iRy EKFfull_K EKFfull_ll EKFfull_Pp EKFfull_rv EKFfull_Ry EKFfullA EKFfullB EKFfullbayes EKFfullbayes EKFfullBM EKFfullC EKFfullD EKFfulldimu EKFfulldimx EKFfulldimy EKFfullE EKFfullEKFfull EKFfullevalll EKFfullevalll EKFfullKalmanFull EKFfullKalmanFull EKFfullll EKFfullll EKFfullmu EKFfulloperator<< EKFfullP EKFfullpfxu EKFfullphxu EKFfullQ EKFfullR EKFfullrv EKFfullset_est EKFfullset_parameters EKFfull~BM