2 Copyright (C) 2008-2014 EDF R&D
4 This file is part of SALOME ADAO module.
6 This library is free software; you can redistribute it and/or
7 modify it under the terms of the GNU Lesser General Public
8 License as published by the Free Software Foundation; either
9 version 2.1 of the License, or (at your option) any later version.
11 This library is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 Lesser General Public License for more details.
16 You should have received a copy of the GNU Lesser General Public
17 License along with this library; if not, write to the Free Software
18 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
20 See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
22 Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 .. index:: single: UnscentedKalmanFilter
25 .. _section_ref_algorithm_UnscentedKalmanFilter:
27 Calculation algorithm "*UnscentedKalmanFilter*"
28 -----------------------------------------------
33 This algorithm realizes an estimation of the state of a dynamic system by a
34 "unscented" Kalman Filter, avoiding to have to perform the tangent and adjoint
35 operators for the observation and evolution operators, as in the simple or
36 extended Kalman filter.
38 Optional and required commands
39 ++++++++++++++++++++++++++++++
41 .. index:: single: Background
42 .. index:: single: BackgroundError
43 .. index:: single: Observation
44 .. index:: single: ObservationError
45 .. index:: single: ObservationOperator
46 .. index:: single: Bounds
47 .. index:: single: ConstrainedBy
48 .. index:: single: EstimationOf
49 .. index:: single: Alpha
50 .. index:: single: Beta
51 .. index:: single: Kappa
52 .. index:: single: Reconditioner
53 .. index:: single: StoreInternalVariables
54 .. index:: single: StoreSupplementaryCalculations
56 The general required commands, available in the editing user interface, are the
60 *Required command*. This indicates the background or initial vector used,
61 previously noted as :math:`\mathbf{x}^b`. Its value is defined as a
62 "*Vector*" or a *VectorSerie*" type object.
65 *Required command*. This indicates the background error covariance matrix,
66 previously noted as :math:`\mathbf{B}`. Its value is defined as a "*Matrix*"
67 type object, a "*ScalarSparseMatrix*" type object, or a
68 "*DiagonalSparseMatrix*" type object.
71 *Required command*. This indicates the observation vector used for data
72 assimilation or optimization, previously noted as :math:`\mathbf{y}^o`. It
73 is defined as a "*Vector*" or a *VectorSerie* type object.
76 *Required command*. This indicates the observation error covariance matrix,
77 previously noted as :math:`\mathbf{R}`. It is defined as a "*Matrix*" type
78 object, a "*ScalarSparseMatrix*" type object, or a "*DiagonalSparseMatrix*"
82 *Required command*. This indicates the observation operator, previously
83 noted :math:`H`, which transforms the input parameters :math:`\mathbf{x}` to
84 results :math:`\mathbf{y}` to be compared to observations
85 :math:`\mathbf{y}^o`. Its value is defined as a "*Function*" type object or
86 a "*Matrix*" type one. In the case of "*Function*" type, different
87 functional forms can be used, as described in the section
88 :ref:`section_ref_operator_requirements`. If there is some control :math:`U`
89 included in the observation, the operator has to be applied to a pair
92 The general optional commands, available in the editing user interface, are
93 indicated in :ref:`section_ref_assimilation_keywords`. In particular, the
94 optional command "*AlgorithmParameters*" allows to choose the specific options,
95 described hereafter, of the algorithm. See
96 :ref:`section_ref_options_AlgorithmParameters` for the good use of this command.
98 The options of the algorithm are the following:
101 This key allows to define upper and lower bounds for every state variable
102 being optimized. Bounds have to be given by a list of list of pairs of
103 lower/upper bounds for each variable, with extreme values every time there
104 is no bound (``None`` is not allowed when there is no bound).
107 This key allows to define the method to take bounds into account. The
108 possible methods are in the following list: ["EstimateProjection"].
111 This key allows to choose the type of estimation to be performed. It can be
112 either state-estimation, with a value of "State", or parameter-estimation,
113 with a value of "Parameters". The default choice is "State".
115 Alpha, Beta, Kappa, Reconditioner
116 These keys are internal scaling parameters. "Alpha" requires a value between
117 1.e-4 and 1. "Beta" has an optimal value of 2 for Gaussian *a priori*
118 distribution. "Kappa" requires an integer value, and the right default is
119 obtained by setting it to 0. "Reconditioner" requires a value between 1.e-3
120 and 10, it defaults to 1.
122 StoreInternalVariables
123 This Boolean key allows to store default internal variables, mainly the
124 current state during iterative optimization process. Be careful, this can be
125 a numerically costly choice in certain calculation cases. The default is
128 StoreSupplementaryCalculations
129 This list indicates the names of the supplementary variables that can be
130 available at the end of the algorithm. It involves potentially costly
131 calculations or memory consumptions. The default is a void list, none of
132 these variables being calculated and stored by default. The possible names
133 are in the following list: ["APosterioriCovariance", "BMA", "Innovation"].
138 References to other sections:
139 - :ref:`section_ref_algorithm_KalmanFilter`
140 - :ref:`section_ref_algorithm_ExtendedKalmanFilter`
142 Bibliographical references: