-
Notifications
You must be signed in to change notification settings - Fork 20
/
Contents.m
134 lines (134 loc) · 6.26 KB
/
Contents.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
% 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