% EKF/UKF toolbox for Matlab 7.x
% Version 1.3, August 12, 2011
%
% Copyright (C) 2005-2011 Simo S�rkk�, <simo.sarkka@hut.fi>
% 2007-2011 Jouni Hartikainen <jmjharti@cc.hut.fi>
% 2010-2011 Arno Solin <arno.solin@tkk.fi>
% 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