% EKF/UKF toolbox for Matlab 7.x % Version 1.3, August 12, 2011 % % Copyright (C) 2005-2011 Simo S�rkk�, % 2007-2011 Jouni Hartikainen % 2010-2011 Arno Solin % History: % 12.08.2011 JH & AS & SS Updated to version 1.3 % 04.09.2007 JH & SS Updated for version 1.1 % 06.08.2007 JH Updated for version 1.0 % % This software is distributed under the GNU General Public % Licence (version 2 or later); please refer to the file % Licence.txt, included with the software, for details. % % % Kalman filtering % KF_PREDICT Perform Kalman Filter prediction step % KF_UPDATE Kalman Filter update step % KF_LHOOD Kalman Filter measurement likelihood % RTS_SMOOTH Rauch-Tung-Striebel Smoother % TF_SMOOTH Smoother based on combination of two Kalman filters % % Extended Kalman filtering % EKF_PREDICT1 1st order Extended Kalman Filter prediction step % EKF_UPDATE1 1st order Extended Kalman Filter update step % EKF_PREDICT2 2nd order Extended Kalman Filter prediction step % EKF_UPDATE2 2nd order Extended Kalman Filter update step % ERTS_SMOOTH1 1st order Extended RTS Smoother % ETF_SMOOTH1 Smoother based on two 1. order extended Kalman filters % % Nonlinear transform based filtering % UT_WEIGHTS Generate weights for sigma points using the summation form % UT_MWEIGTS Generate weights for sigma points using the matrix form % UT_SIGMAS Generate Sigma Points for Unscented Transformation % UT_TRANSFORM Makes the Unscented Transformation of x and y % UKF_PREDICT1 Nonaugmented UKF prediction step % UKF_UPDATE1 Nonaugmented UKF update step % UKF_PREDICT2 Augmented (state and process noise) UKF prediction step % UKF_UPDATE2 Augmented (state and measurement noise) UKF update step % UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step % UKF_UPDATE3 Augmented (state, process and measurement noise) UKF update step % URTS_SMOOTH1 Nonaugmented unscented RTS-smoother % URTS_SMOOTH2 Augmented unscented RTS-smoother % UTF_SMOOTH Smoother based on combination of two unscented Kalman filters % GH_TRANSFORM Gauss-Hermite transform of random variables % GHKF_PREDICT Gauss-Hermite Kalman filter prediction step % GHKF_UPDATE Gauss-Hermite Kalman filter update step % GHRTS_SMOOTH Additive form Gauss-Hermite Rauch-Tung-Striebel smoother % CKF_TRANSFORM Cubature Kalman filter transform of random variables % CKF_PREDICT Cubature Kalman filter prediction step % CKF_UPDATE Cubature Kalman filter update step % CRTS_SMOOTH - Additive form cubature Rauch-Tung-Striebel smoother % % Multiple Model Filtering % IMM_PREDICT IMM filter prediction step % IMM_UPDATE IMM filter update step % IMM_SMOOTH IMM smoothing % EIMM_PREDICT IMM-EKF filter prediction step % EIMM_UPDATE IMM-EKF filter update step % EIMM_SMOOTH IMM-EKF smoothing % UIMM_PREDICT IMM-UKF filter prediction step % UIMM_UPDATE IMM-UKF filter update step % UIMM_SMOOTH IMM-UKF smoothing % % % Misc. % GAUSS_PDF Multivariate Gaussian PDF % GAUSS_RND Multivariate Gaussian random variables % LTI_INT Integrate LTI ODE with Gaussian Noise % LTI_DISC Discretize LTI ODE with Gaussian Noise % RK4 Runge-Kutta integration % DER_CHECK Check derivatives using finite differences % SCHOL Positive semidefinite matrix Cholesky factorization % RESAMPSTR Stratified resampling % % /DEMOS/ % % /KF_CWPA_DEMO/ % KF_CWPA_DEMO CWPA model demonstration with Kalman filter % % /EKF_SINE_DEMO/ % EKF_SINE_F Dynamic model function (needed by the augmented UKF) % EKF_SINE_H Measurement model function % EKF_SINE_DH_DX 1st order derivative of the measurement model % EKF_SINE_D2H_DX2 2nd order derivative of the measurement model % EKF_SINE_DEMO Random Sine Signal demonstration % % /UNGM_DEMO/ % UNGM_F Dynamic model function % UNGM_DF_DX 1st order derivative of the dynamic model % UNGM_D2F_DX2 2nd order derivative of the dynamic model (not used) % UNGM_H Measurement model function % UNGM_DH_DX 1st order derivative of the measurement model % UNGM_D2H_DX2 2nd order derivative of the measurement model (not used) % UNGM_DEMO UNGM model demonstration % % /BOT_DEMO/ % BOT_H Measurement model function % BOT_DH_DX 1st order derivative of the measurement model % BOT_D2H_DX2 2nd order derivative of the measurement model % BOT_DEMO_ALL BOT demo with EKF and UKF % EKFS_BOT_DEMO BOT demo with EKF % UKFS_BOT_DEMO BOT demo with UKF % GHKFS_BOT_DEMO BOT demo with GHKF % CKFS_BOT_DEMO BOT demo with CKF % % /REENTRY_DEMO/ % REENTRY_F Dynamic model function % REENTRY_DF Derivative of the dynamic model % REENTRY_H Measurement model function % REENTRY_DH Derivative of the measurement model % REENTRY_IF Inverse prediction of the dynamic model % REENTRY_COND Generates condition numbers for simulation data % MAKE_REENTRY_DATA Generates the simulation data for reentry dynamics % REENTRY_DEMO Reentry Vehicle Tracking demonstration % % /IMM_DEMO/ % IMM_DEMO Tracking a Target with Simple Manouvers demonstration % % /EIMM_DEMO/ % F_TURN Dynamic model function for the coordinated turn model % F_TURN_DX Jacobian of the coordinated turn model's dynamic model % F_TURN_INV Inverse dynamics of the coordinated turn model % CT_DEMO Coordinated Turn Model demonstration % BOT_H Measurement model function % BOT_DH_DX 1st order derivative of the measurement model % BOT_D2H_DX2 2nd order derivative of the measurement model % BOTM_DEMO Bearings Only Tracking of a Manouvering Target Demonstration % % Demos currently included in the toolbox, but not documented: % % /KF_SINE_DEMO/ % KF_SINE_DEMO Sine signal demonstration with Kalman filter