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 8f135dc601bfab9d079057af333abf19edea3d49..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
@@ -89,10 +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:
 
@@ -104,6 +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,
@@ -125,10 +134,12 @@ The options of the algorithm are the following:
     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", "CostFunctionJ",
-    "CurrentState", "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
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
@@ -151,12 +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.