EKFCh KalmanCh EKF_unQ EKF_unQ EKF_unQ libKF.h diffbifn * diffbifn* EKFCh::pfxu pfxu Internal Model f(x,u). diffbifn * diffbifn* EKFCh::phxu phxu Observation Model h(x,u). EKFCh::EKFCh (RV rvx, RV rvy, RV rvu) EKFCh RV rvx RV rvy RV rvu Default constructor. void void EKFCh::set_parameters (diffbifn *pfxu, diffbifn *phxu, const chmat Q0, const chmat R0) set_parameters diffbifn * pfxu diffbifn * phxu const chmat Q0 const chmat R0 Set nonlinear functions for mean values and covariance matrices. chmat::_Ch Kalman< chmat >::_mu Kalman< chmat >::A Kalman< chmat >::B Kalman< chmat >::C Kalman< chmat >::D diffbifn::dfdx_cond Kalman< chmat >::dimu Kalman< chmat >::dimx Kalman< chmat >::dimy KalmanCh::preA Kalman< chmat >::Q Kalman< chmat >::R void void EKFCh::bayes (const vec &dt) bayes bayes const vec & dt Here dt = [yt;ut] of appropriate dimensions. chmat::_Ch Kalman< chmat >::_K Kalman< chmat >::_mu Kalman< chmat >::_P Kalman< chmat >::_Ry Kalman< chmat >::_yp Kalman< chmat >::A Kalman< chmat >::C diffbifn::dfdx_cond Kalman< chmat >::dimu Kalman< chmat >::dimx Kalman< chmat >::dimy diffbifn::eval BM::evalll enorm< sq_T >::evalpdflog Kalman< chmat >::fy BM::ll KalmanCh::postA KalmanCh::preA chmat::to_mat void void KalmanCh::set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &R0, const chmat &Q0) set_parameters set_parameters const mat & A0 const mat & B0 const mat & C0 const mat & D0 const chmat & R0 const chmat & Q0 Set parameters with check of relevance. chmat::_Ch Kalman< chmat >::dimx Kalman< chmat >::dimy KalmanCh::preA Kalman< chmat >::Q Kalman< chmat >::R void void KalmanCh::set_est (const vec &mu0, const chmat &P0) set_est set_est const vec & mu0 const chmat & P0 Set estimate values, used e.g. in initialization. Kalman< chmat >::est enorm< sq_T >::set_parameters void void BM::bayes (mat Dt) bayes mat Dt Batch Bayes rule (columns of Dt are observations). epdf & epdf& Kalman< chmat >::_epdf () _epdf _epdf access function mat & mat& Kalman< chmat >::__K () __K access function vec vec Kalman< chmat >::_dP () _dP access function const RV & const RV& BM::_rv () const _rv access function BM::rv double double BM::_ll () const _ll access function BM::ll mat mat KalmanCh::preA preA pre array (triangular matrix) bayes KalmanCh::bayes EKF_unQ::condition set_parameters KalmanCh::set_parameters mat mat KalmanCh::postA postA post array (triangular matrix) bayes KalmanCh::bayes RV RV Kalman< chmat >::rvy rvy Indetifier of output rv. RV RV Kalman< chmat >::rvu rvu Indetifier of exogeneous rv. int int Kalman< chmat >::dimx dimx cache of rv.count() bayes KalmanCh::bayes EKF_unQ::condition set_parameters KalmanCh::set_parameters int int Kalman< chmat >::dimy dimy cache of rvy.count() bayes KalmanCh::bayes EKF_unQ::condition set_parameters KalmanCh::set_parameters int int Kalman< chmat >::dimu dimu cache of rvu.count() bayes KalmanCh::bayes set_parameters mat mat Kalman< chmat >::A A Matrix A. bayes KalmanCh::bayes set_parameters mat mat Kalman< chmat >::B B Matrix B. KalmanCh::bayes set_parameters mat mat Kalman< chmat >::C C Matrix C. bayes KalmanCh::bayes set_parameters mat mat Kalman< chmat >::D D Matrix D. KalmanCh::bayes set_parameters chmat chmat Kalman< chmat >::Q Q Matrix Q in square-root form. EKF_unQ::condition set_parameters KalmanCh::set_parameters chmat chmat Kalman< chmat >::R R Matrix R in square-root form. set_parameters KalmanCh::set_parameters enorm< chmat > enorm<chmat > Kalman< chmat >::est est posterior density on $x_t$ KalmanCh::set_est enorm< chmat > enorm<chmat > Kalman< chmat >::fy fy preditive density on $y_t$ bayes KalmanCh::bayes mat mat Kalman< chmat >::_K _K placeholder for Kalman gain bayes KalmanCh::bayes vec & vec& Kalman< chmat >::_yp _yp cache of fy.mu bayes KalmanCh::bayes chmat & chmat & Kalman< chmat >::_Ry _Ry cache of fy.R bayes KalmanCh::bayes vec & vec& Kalman< chmat >::_mu _mu cache of est.mu bayes KalmanCh::bayes set_parameters chmat & chmat & Kalman< chmat >::_P _P cache of est.R bayes KalmanCh::bayes RV RV BM::rv rv Random variable of the posterior. BM::_rv MPF< BM_T >::MPF EKFfull::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 bayes KalmanCh::bayes EKFfull::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 bayes KalmanCh::bayes EKFfull::bayes ARX::bayes Extended Kalman Filter in Square root. An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. EKFCh BM EKF_unQ KalmanCh Kalman< chmat > < chmat > Kalman< sq_T > enorm< sq_T > R EKFCh pfxu phxu sq_T epdf rv BM rv RV sqmat KalmanCh diffbifn rvu rvx Kalman< chmat > _P Q R _Ry rvu rvy est fy < chmat > enorm< chmat > R < chmat > Kalman< sq_T > _P Q R _Ry rvu rvy chmat fnc eEF EKFCh__K EKFCh_dP EKFCh_epdf EKFCh_K EKFCh_ll EKFCh_mu EKFCh_P EKFCh_rv EKFCh_Ry EKFCh_yp EKFChA EKFChB EKFChbayes EKFChbayes EKFChBM EKFChC EKFChD EKFChdimu EKFChdimx EKFChdimy EKFChEKFCh EKFChest EKFChevalll EKFChfy EKFChKalman EKFChKalman EKFChKalmanCh EKFChll EKFChpfxu EKFChphxu EKFChpostA EKFChpreA EKFChQ EKFChR EKFChrv EKFChrvu EKFChrvy EKFChset_est EKFChset_parameters EKFChset_parameters EKFCh~BM