Documentation minor correction

[modules/adao.git] / doc / en / ref_algorithm_UnscentedKalmanFilter.rst

diff --git a/doc/en/ref_algorithm_UnscentedKalmanFilter.rst b/doc/en/ref_algorithm_UnscentedKalmanFilter.rst
index cf3b45ba415d1b2b75435074b2dc4e7390d22a52..cb5343c236003f65990e560d68598e732ee7c732 100644 (file)
--- a/doc/en/ref_algorithm_UnscentedKalmanFilter.rst
+++ b/doc/en/ref_algorithm_UnscentedKalmanFilter.rst
@@ -1,5 +1,5 @@
 ..
-   Copyright (C) 2008-2015 EDF R&D
+   Copyright (C) 2008-2016 EDF R&D
 
    This file is part of SALOME ADAO module.
 
@@ -38,6 +38,7 @@ extended Kalman filter.
 Optional and required commands
 ++++++++++++++++++++++++++++++
 
+.. index:: single: AlgorithmParameters
 .. index:: single: Background
 .. index:: single: BackgroundError
 .. index:: single: Observation
@@ -50,7 +51,6 @@ Optional and required commands
 .. index:: single: Beta
 .. index:: single: Kappa
 .. index:: single: Reconditioner
-.. index:: single: StoreInternalVariables
 .. index:: single: StoreSupplementaryCalculations
 
 The general required commands, available in the editing user interface, are the
@@ -90,10 +90,11 @@ following:
     :math:`(X,U)`.
 
 The general optional commands, available in the editing user interface, are
-indicated in :ref:`section_ref_assimilation_keywords`. In particular, the
-optional command "*AlgorithmParameters*" allows to choose the specific options,
+indicated in :ref:`section_ref_assimilation_keywords`. Moreover, the parameters
+of the command "*AlgorithmParameters*" allows to choose the specific options,
 described hereafter, of the algorithm. See
-:ref:`section_ref_options_AlgorithmParameters` for the good use of this command.
+:ref:`section_ref_options_Algorithm_Parameters` for the good use of this
+command.
 
 The options of the algorithm are the following:
 
@@ -105,6 +106,13 @@ The options of the algorithm are the following:
 
     Example : ``{"Bounds":[[2.,5.],[1.e-2,10.],[-30.,1.e99],[-1.e99,1.e99]]}``
 
+  ConstrainedBy
+    This key allows to choose the method to take into account the bounds
+    constraints. The only one available is the "EstimateProjection", which
+    projects the current state estimate on the bounds constraints.
+
+    Example : ``{"ConstrainedBy":"EstimateProjection"}``
+
   EstimationOf
     This key allows to choose the type of estimation to be performed. It can be
     either state-estimation, with a value of "State", or parameter-estimation,
@@ -121,22 +129,17 @@ The options of the algorithm are the following:
 
     Example : ``{"Alpha":1,"Beta":2,"Kappa":0,"Reconditioner":1}``
 
-  StoreInternalVariables
-    This Boolean key allows to store default internal variables, mainly the
-    current state during iterative optimization process. Be careful, this can be
-    a numerically costly choice in certain calculation cases. The default is
-    "False".
-
-    Example : ``{"StoreInternalVariables":True}``
-
   StoreSupplementaryCalculations
     This list indicates the names of the supplementary variables that can be
     available at the end of the algorithm. It involves potentially costly
     calculations or memory consumptions. The default is a void list, none of
     these variables being calculated and stored by default. The possible names
-    are in the following list: ["APosterioriCovariance", "BMA", "Innovation"].
+    are in the following list: ["APosterioriCorrelations",
+    "APosterioriCovariance", "APosterioriStandardDeviations",
+    "APosterioriVariances", "BMA", "CostFunctionJ", "CurrentState",
+    "Innovation"].
 
-    Example : ``{"StoreSupplementaryCalculations":["BMA","Innovation"]}``
+    Example : ``{"StoreSupplementaryCalculations":["BMA", "Innovation"]}``
 
 Information and variables available at the end of the algorithm
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
@@ -159,12 +162,30 @@ The unconditional outputs of the algorithm are the following:
 
 The conditional outputs of the algorithm are the following:
 
+  APosterioriCorrelations
+    *List of matrices*. Each element is an *a posteriori* error correlation
+    matrix of the optimal state.
+
+    Example : ``C = ADD.get("APosterioriCorrelations")[-1]``
+
   APosterioriCovariance
     *List of matrices*. Each element is an *a posteriori* error covariance
     matrix :math:`\mathbf{A}*` of the optimal state.
 
     Example : ``A = ADD.get("APosterioriCovariance")[-1]``
 
+  APosterioriStandardDeviations
+    *List of matrices*. Each element is an *a posteriori* error standard
+    deviation matrix of the optimal state.
+
+    Example : ``E = ADD.get("APosterioriStandardDeviations")[-1]``
+
+  APosterioriVariances
+    *List of matrices*. Each element is an *a posteriori* error variance matrix
+    of the optimal state.
+
+    Example : ``V = ADD.get("APosterioriVariances")[-1]``
+
   BMA
     *List of vectors*. Each element is a vector of difference between the
     background and the optimal state.