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    * Licensed to CS GROUP (CS) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * CS licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *   http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
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   * See the License for the specific language governing permissions and
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  package org.orekit.estimation.sequential;
  
  import org.hipparchus.filtering.kalman.ProcessEstimate;
  import org.hipparchus.filtering.kalman.unscented.UnscentedEvolution;
  import org.hipparchus.filtering.kalman.unscented.UnscentedKalmanFilter;
  import org.hipparchus.filtering.kalman.unscented.UnscentedProcess;
  import org.hipparchus.linear.ArrayRealVector;
  import org.hipparchus.linear.MatrixUtils;
  import org.hipparchus.linear.RealMatrix;
  import org.hipparchus.linear.RealVector;
  import org.hipparchus.util.FastMath;
  import org.orekit.estimation.measurements.EstimatedMeasurement;
  import org.orekit.estimation.measurements.EstimatedMeasurementBase;
  import org.orekit.estimation.measurements.ObservedMeasurement;
  import org.orekit.orbits.Orbit;
  import org.orekit.orbits.OrbitType;
  import org.orekit.orbits.PositionAngleType;
  import org.orekit.propagation.PropagationType;
  import org.orekit.propagation.SpacecraftState;
  import org.orekit.propagation.conversion.DSSTPropagatorBuilder;
  import org.orekit.propagation.semianalytical.dsst.DSSTPropagator;
  import org.orekit.propagation.semianalytical.dsst.forces.DSSTForceModel;
  import org.orekit.propagation.semianalytical.dsst.forces.ShortPeriodTerms;
  import org.orekit.propagation.semianalytical.dsst.utilities.AuxiliaryElements;
  import org.orekit.time.AbsoluteDate;
  import org.orekit.time.ChronologicalComparator;
  import org.orekit.utils.ParameterDriver;
  import org.orekit.utils.ParameterDriversList;
  import org.orekit.utils.ParameterDriversList.DelegatingDriver;
  
  import java.util.ArrayList;
  import java.util.Comparator;
  import java.util.List;
  
  /** Class defining the process model dynamics to use with a {@link SemiAnalyticalUnscentedKalmanEstimator}.
   * @author Gaëtan Pierre
   * @author Bryan Cazabonne
   * @since 11.3
   */
  public class SemiAnalyticalUnscentedKalmanModel implements KalmanEstimation, UnscentedProcess<MeasurementDecorator>, SemiAnalyticalProcess {
  
      /** Initial builder for propagator. */
      private final DSSTPropagatorBuilder builder;
  
      /** Estimated orbital parameters. */
      private final ParameterDriversList estimatedOrbitalParameters;
  
      /** Estimated propagation parameters. */
      private final ParameterDriversList estimatedPropagationParameters;
  
      /** Estimated measurements parameters. */
      private final ParameterDriversList estimatedMeasurementsParameters;
  
      /** Provider for covariance matrice. */
      private final CovarianceMatrixProvider covarianceMatrixProvider;
  
      /** Process noise matrix provider for measurement parameters. */
      private final CovarianceMatrixProvider measurementProcessNoiseMatrix;
  
      /** Position angle type used during orbit determination. */
      private final PositionAngleType angleType;
  
      /** Orbit type used during orbit determination. */
      private final OrbitType orbitType;
  
      /** Current corrected estimate. */
      private ProcessEstimate correctedEstimate;
  
      /** Observer to retrieve current estimation info. */
      private KalmanObserver observer;
  
      /** Current number of measurement. */
      private int currentMeasurementNumber;
  
      /** Current date. */
      private AbsoluteDate currentDate;
  
      /** Nominal mean spacecraft state. */
      private SpacecraftState nominalMeanSpacecraftState;
  
      /** Previous nominal mean spacecraft state. */
      private SpacecraftState previousNominalMeanSpacecraftState;
  
     /** Predicted osculating spacecraft state. */
     private SpacecraftState predictedSpacecraftState;
 
     /** Corrected mean spacecraft state. */
     private SpacecraftState correctedSpacecraftState;
 
     /** Predicted measurement. */
     private EstimatedMeasurement<?> predictedMeasurement;
 
     /** Corrected measurement. */
     private EstimatedMeasurement<?> correctedMeasurement;
 
     /** Predicted mean element filter correction. */
     private RealVector predictedFilterCorrection;
 
     /** Corrected mean element filter correction. */
     private RealVector correctedFilterCorrection;
 
     /** Propagators for the reference trajectories, up to current date. */
     private DSSTPropagator dsstPropagator;
 
     /** Short period terms for the nominal mean spacecraft state. */
     private RealVector shortPeriodicTerms;
 
     /** Unscented Kalman process model constructor (package private).
      * @param propagatorBuilder propagators builders used to evaluate the orbits.
      * @param covarianceMatrixProvider provider for covariance matrix
      * @param estimatedMeasurementParameters measurement parameters to estimate
      * @param measurementProcessNoiseMatrix provider for measurement process noise matrix
      */
     protected SemiAnalyticalUnscentedKalmanModel(final DSSTPropagatorBuilder propagatorBuilder,
                                                  final CovarianceMatrixProvider covarianceMatrixProvider,
                                                  final ParameterDriversList estimatedMeasurementParameters,
                                                  final CovarianceMatrixProvider measurementProcessNoiseMatrix) {
 
         this.builder                         = propagatorBuilder;
         this.angleType                       = propagatorBuilder.getPositionAngleType();
         this.orbitType                       = propagatorBuilder.getOrbitType();
         this.estimatedMeasurementsParameters = estimatedMeasurementParameters;
         this.currentMeasurementNumber        = 0;
         this.currentDate                     = propagatorBuilder.getInitialOrbitDate();
         this.covarianceMatrixProvider        = covarianceMatrixProvider;
         this.measurementProcessNoiseMatrix   = measurementProcessNoiseMatrix;
 
         // Number of estimated parameters
         int columns = 0;
 
         // Set estimated orbital parameters
         this.estimatedOrbitalParameters = new ParameterDriversList();
         for (final ParameterDriver driver : propagatorBuilder.getOrbitalParametersDrivers().getDrivers()) {
 
             // Verify if the driver reference date has been set
             if (driver.getReferenceDate() == null) {
                 driver.setReferenceDate(currentDate);
             }
 
             // Verify if the driver is selected
             if (driver.isSelected()) {
                 estimatedOrbitalParameters.add(driver);
                 columns++;
             }
 
         }
 
         // Set estimated propagation parameters
         this.estimatedPropagationParameters = new ParameterDriversList();
         final List<String> estimatedPropagationParametersNames = new ArrayList<>();
         for (final ParameterDriver driver : propagatorBuilder.getPropagationParametersDrivers().getDrivers()) {
 
             // Verify if the driver reference date has been set
             if (driver.getReferenceDate() == null) {
                 driver.setReferenceDate(currentDate);
             }
 
             // Verify if the driver is selected
             if (driver.isSelected()) {
                 estimatedPropagationParameters.add(driver);
                 final String driverName = driver.getName();
                 // Add the driver name if it has not been added yet
                 if (!estimatedPropagationParametersNames.contains(driverName)) {
                     estimatedPropagationParametersNames.add(driverName);
                     ++columns;
                 }
             }
 
         }
         estimatedPropagationParametersNames.sort(Comparator.naturalOrder());
 
         // Set the estimated measurement parameters
         for (final ParameterDriver parameter : estimatedMeasurementsParameters.getDrivers()) {
             if (parameter.getReferenceDate() == null) {
                 parameter.setReferenceDate(currentDate);
             }
             ++columns;
         }
 
         // Number of estimated measurement parameters
         final int nbMeas = estimatedMeasurementParameters.getNbParams();
 
         // Number of estimated dynamic parameters (orbital + propagation)
         final int nbDyn  = estimatedOrbitalParameters.getNbParams() +
                            estimatedPropagationParameters.getNbParams();
 
         // Build the reference propagator
         this.dsstPropagator = getEstimatedPropagator();
         final SpacecraftState meanState = dsstPropagator.initialIsOsculating() ?
                          DSSTPropagator.computeMeanState(dsstPropagator.getInitialState(), dsstPropagator.getAttitudeProvider(), dsstPropagator.getAllForceModels()) :
                          dsstPropagator.getInitialState();
         this.nominalMeanSpacecraftState         = meanState;
         this.predictedSpacecraftState           = meanState;
         this.correctedSpacecraftState           = predictedSpacecraftState;
         this.previousNominalMeanSpacecraftState = nominalMeanSpacecraftState;
 
         // Initialize the estimated mean element filter correction
         this.predictedFilterCorrection = MatrixUtils.createRealVector(columns);
         this.correctedFilterCorrection = predictedFilterCorrection;
 
         // Covariance matrix
         final RealMatrix noiseK = MatrixUtils.createRealMatrix(nbDyn + nbMeas, nbDyn + nbMeas);
         final RealMatrix noiseP = covarianceMatrixProvider.getInitialCovarianceMatrix(nominalMeanSpacecraftState);
         noiseK.setSubMatrix(noiseP.getData(), 0, 0);
         if (measurementProcessNoiseMatrix != null) {
             final RealMatrix noiseM = measurementProcessNoiseMatrix.getInitialCovarianceMatrix(nominalMeanSpacecraftState);
             noiseK.setSubMatrix(noiseM.getData(), nbDyn, nbDyn);
         }
 
         KalmanEstimatorUtil.checkDimension(noiseK.getRowDimension(),
                                            propagatorBuilder.getOrbitalParametersDrivers(),
                                            propagatorBuilder.getPropagationParametersDrivers(),
                                            estimatedMeasurementsParameters);
 
         // Initialize corrected estimate
         this.correctedEstimate = new ProcessEstimate(0.0, correctedFilterCorrection, noiseK);
 
     }
 
     /** {@inheritDoc} */
     @Override
     public KalmanObserver getObserver() {
         return observer;
     }
 
     /** Set the observer.
      * @param observer the observer
      */
     public void setObserver(final KalmanObserver observer) {
         this.observer = observer;
     }
 
     /** Get the current corrected estimate.
      * <p>
      * For the Unscented Semi-analytical Kalman Filter
      * it corresponds to the corrected filter correction.
      * In other words, it doesn't represent an orbital state.
      * </p>
      * @return current corrected estimate
      */
     public ProcessEstimate getEstimate() {
         return correctedEstimate;
     }
 
     /** Process measurements.
      * @param observedMeasurements the list of measurements to process
      * @param filter Unscented Kalman Filter
      * @return estimated propagator
      */
     public DSSTPropagator processMeasurements(final List<ObservedMeasurement<?>> observedMeasurements,
                                               final UnscentedKalmanFilter<MeasurementDecorator> filter) {
 
         // Sort the measurement
         observedMeasurements.sort(new ChronologicalComparator());
         final AbsoluteDate tStart             = observedMeasurements.get(0).getDate();
         final AbsoluteDate tEnd               = observedMeasurements.get(observedMeasurements.size() - 1).getDate();
         final double       overshootTimeRange = FastMath.nextAfter(tEnd.durationFrom(tStart),
                                                 Double.POSITIVE_INFINITY);
 
         // Initialize step handler and set it to a parallelized propagator
         final SemiAnalyticalMeasurementHandler  stepHandler = new SemiAnalyticalMeasurementHandler(this, filter, observedMeasurements, builder.getInitialOrbitDate(), true);
         dsstPropagator.getMultiplexer().add(stepHandler);
         dsstPropagator.propagate(tStart, tStart.shiftedBy(overshootTimeRange));
 
         // Return the last estimated propagator
         return getEstimatedPropagator();
 
     }
 
     /** Get the propagator estimated with the values set in the propagator builder.
      * @return propagator based on the current values in the builder
      */
     public DSSTPropagator getEstimatedPropagator() {
         // Return propagator built with current instantiation of the propagator builder
         return (DSSTPropagator) builder.buildPropagator();
     }
 
     /** {@inheritDoc} */
     @Override
     public UnscentedEvolution getEvolution(final double previousTime, final RealVector[] sigmaPoints,
                                            final MeasurementDecorator measurement) {
 
         // Set a reference date for all measurements parameters that lack one (including the not estimated ones)
         final ObservedMeasurement<?> observedMeasurement = measurement.getObservedMeasurement();
         for (final ParameterDriver driver : observedMeasurement.getParametersDrivers()) {
             if (driver.getReferenceDate() == null) {
                 driver.setReferenceDate(builder.getInitialOrbitDate());
             }
         }
 
         // Increment measurement number
         ++currentMeasurementNumber;
 
         // Update the current date
         currentDate = measurement.getObservedMeasurement().getDate();
 
         // STM for the prediction of the filter correction
         final RealMatrix stm = getStm();
 
         // Predicted states
         final RealVector[] predictedStates = new RealVector[sigmaPoints.length];
         for (int k = 0; k < sigmaPoints.length; ++k) {
             // Predict filter correction for the current sigma point
             final RealVector predicted = stm.operate(sigmaPoints[k]);
             predictedStates[k] = predicted;
         }
 
         // Number of estimated measurement parameters
         final int nbMeas = getNumberSelectedMeasurementDrivers();
 
         // Number of estimated dynamic parameters (orbital + propagation)
         final int nbDyn  = getNumberSelectedOrbitalDrivers() + getNumberSelectedPropagationDrivers();
 
         // Covariance matrix
         final RealMatrix noiseK = MatrixUtils.createRealMatrix(nbDyn + nbMeas, nbDyn + nbMeas);
         final RealMatrix noiseP = covarianceMatrixProvider.getProcessNoiseMatrix(previousNominalMeanSpacecraftState, nominalMeanSpacecraftState);
         noiseK.setSubMatrix(noiseP.getData(), 0, 0);
         if (measurementProcessNoiseMatrix != null) {
             final RealMatrix noiseM = measurementProcessNoiseMatrix.getProcessNoiseMatrix(previousNominalMeanSpacecraftState, nominalMeanSpacecraftState);
             noiseK.setSubMatrix(noiseM.getData(), nbDyn, nbDyn);
         }
 
         // Verify dimension
         KalmanEstimatorUtil.checkDimension(noiseK.getRowDimension(),
                                            builder.getOrbitalParametersDrivers(),
                                            builder.getPropagationParametersDrivers(),
                                            estimatedMeasurementsParameters);
 
         // Return
         return new UnscentedEvolution(measurement.getTime(), predictedStates, noiseK);
 
     }
 
     /** {@inheritDoc} */
     @Override
     public RealVector[] getPredictedMeasurements(final RealVector[] predictedSigmaPoints, final MeasurementDecorator measurement) {
 
         // Observed measurement
         final ObservedMeasurement<?> observedMeasurement = measurement.getObservedMeasurement();
 
         // Initialize arrays of predicted states and measurements
         final RealVector[] predictedMeasurements = new RealVector[predictedSigmaPoints.length];
 
         // Loop on sigma points
         for (int k = 0; k < predictedSigmaPoints.length; ++k) {
 
             // Calculate the predicted osculating elements for the current mean state
             final RealVector osculating = computeOsculatingElements(predictedSigmaPoints[k], nominalMeanSpacecraftState, shortPeriodicTerms);
             final Orbit osculatingOrbit = orbitType.mapArrayToOrbit(osculating.toArray(), null, angleType,
                                                                     currentDate, builder.getMu(), builder.getFrame());
 
             // Then, estimate the measurement
             final EstimatedMeasurement<?> estimated = estimateMeasurement(observedMeasurement, currentMeasurementNumber,
                 new SpacecraftState[] { new SpacecraftState(osculatingOrbit) });
             predictedMeasurements[k] = new ArrayRealVector(estimated.getEstimatedValue());
 
         }
 
         // Return
         return predictedMeasurements;
 
     }
 
     /** {@inheritDoc} */
     @Override
     public RealVector getInnovation(final MeasurementDecorator measurement, final RealVector predictedMeas,
                                     final RealVector predictedState, final RealMatrix innovationCovarianceMatrix) {
 
         // Predicted filter correction
         predictedFilterCorrection = predictedState;
 
         // Predicted measurement
         final RealVector osculating = computeOsculatingElements(predictedFilterCorrection, nominalMeanSpacecraftState, shortPeriodicTerms);
         final Orbit osculatingOrbit = orbitType.mapArrayToOrbit(osculating.toArray(), null, angleType,
                                                                 currentDate, builder.getMu(), builder.getFrame());
         predictedSpacecraftState = new SpacecraftState(osculatingOrbit);
         predictedMeasurement = estimateMeasurement(measurement.getObservedMeasurement(), currentMeasurementNumber,
             getPredictedSpacecraftStates());
         predictedMeasurement.setEstimatedValue(predictedMeas.toArray());
 
         // Apply the dynamic outlier filter, if it exists
         KalmanEstimatorUtil.applyDynamicOutlierFilter(predictedMeasurement, innovationCovarianceMatrix);
 
         // Compute the innovation vector (not normalized for unscented Kalman filter)
         return KalmanEstimatorUtil.computeInnovationVector(predictedMeasurement);
 
     }
 
 
     /** {@inheritDoc} */
     @Override
     public void finalizeEstimation(final ObservedMeasurement<?> observedMeasurement,
                                    final ProcessEstimate estimate) {
         // Update the process estimate
         correctedEstimate = estimate;
         // Corrected filter correction
         correctedFilterCorrection = estimate.getState();
 
         // Update the previous nominal mean spacecraft state
         previousNominalMeanSpacecraftState = nominalMeanSpacecraftState;
 
         // Update the previous nominal mean spacecraft state
         // Calculate the corrected osculating elements
         final RealVector osculating = computeOsculatingElements(correctedFilterCorrection, nominalMeanSpacecraftState, shortPeriodicTerms);
         final Orbit osculatingOrbit = orbitType.mapArrayToOrbit(osculating.toArray(), null, builder.getPositionAngleType(),
                                                                 currentDate, builder.getMu(), builder.getFrame());
 
         // Compute the corrected measurements
         correctedSpacecraftState = new SpacecraftState(osculatingOrbit);
         correctedMeasurement = estimateMeasurement(observedMeasurement, currentMeasurementNumber,
             getCorrectedSpacecraftStates());
 
         // Call the observer if the user add one
         if (observer != null) {
             observer.evaluationPerformed(this);
         }
     }
 
     /**
      * Estimate measurement (without derivatives).
      * @param <T> measurement type
      * @param observedMeasurement observed measurement
      * @param measurementNumber measurement number
      * @param spacecraftStates states
      * @return estimated measurements
      * @since 12.1
      */
     private static <T extends ObservedMeasurement<T>> EstimatedMeasurement<?> estimateMeasurement(final ObservedMeasurement<T> observedMeasurement,
                                                                                                   final int measurementNumber,
                                                                                                   final SpacecraftState[] spacecraftStates) {
         final EstimatedMeasurementBase<T> estimatedMeasurementBase = observedMeasurement.
                 estimateWithoutDerivatives(measurementNumber, measurementNumber,
                         KalmanEstimatorUtil.filterRelevant(observedMeasurement, spacecraftStates));
         final EstimatedMeasurement<T> estimatedMeasurement = new EstimatedMeasurement<>(estimatedMeasurementBase.getObservedMeasurement(),
                 estimatedMeasurementBase.getIteration(), estimatedMeasurementBase.getCount(),
                 estimatedMeasurementBase.getStates(), estimatedMeasurementBase.getParticipants());
         estimatedMeasurement.setEstimatedValue(estimatedMeasurementBase.getEstimatedValue());
         return estimatedMeasurement;
     }
 
     /** Get the state transition matrix used to predict the filter correction.
      * <p>
      * The state transition matrix is not computed by the DSST propagator.
      * It is analytically calculated considering Keplerian contribution only
      * </p>
      * @return the state transition matrix used to predict the filter correction
      */
     private RealMatrix getStm() {
 
         // initialize the STM
         final int nbDym  = getNumberSelectedOrbitalDrivers() + getNumberSelectedPropagationDrivers();
         final int nbMeas = getNumberSelectedMeasurementDrivers();
         final RealMatrix stm = MatrixUtils.createRealIdentityMatrix(nbDym + nbMeas);
 
         // State transition matrix using Keplerian contribution only
         final double mu  = builder.getMu();
         final double sma = previousNominalMeanSpacecraftState.getA();
         final double dt  = currentDate.durationFrom(previousNominalMeanSpacecraftState.getDate());
         final double contribution = -1.5 * dt * FastMath.sqrt(mu / FastMath.pow(sma, 5));
         stm.setEntry(5, 0, contribution);
 
         // Return
         return stm;
 
     }
 
     /** {@inheritDoc} */
     @Override
     public void finalizeOperationsObservationGrid() {
         // Update parameters
         updateParameters();
     }
 
     /** {@inheritDoc} */
     @Override
     public ParameterDriversList getEstimatedOrbitalParameters() {
         return estimatedOrbitalParameters;
     }
 
     /** {@inheritDoc} */
     @Override
     public ParameterDriversList getEstimatedPropagationParameters() {
         return estimatedPropagationParameters;
     }
 
     /** {@inheritDoc} */
     @Override
     public ParameterDriversList getEstimatedMeasurementsParameters() {
         return estimatedMeasurementsParameters;
     }
 
     /** {@inheritDoc}
      * <p>
      * Predicted state is osculating.
      * </p>
      */
     @Override
     public SpacecraftState[] getPredictedSpacecraftStates() {
         return new SpacecraftState[] {predictedSpacecraftState};
     }
 
     /** {@inheritDoc}
      * <p>
      * Corrected state is osculating.
      * </p>
      */
     @Override
     public SpacecraftState[] getCorrectedSpacecraftStates() {
         return new SpacecraftState[] {correctedSpacecraftState};
     }
 
     /** {@inheritDoc} */
     @Override
     public RealVector getPhysicalEstimatedState() {
         // Method {@link ParameterDriver#getValue()} is used to get
         // the physical values of the state.
         // The scales'array is used to get the size of the state vector
         final RealVector physicalEstimatedState = new ArrayRealVector(getEstimate().getState().getDimension());
         int i = 0;
         for (final DelegatingDriver driver : getEstimatedOrbitalParameters().getDrivers()) {
             physicalEstimatedState.setEntry(i++, driver.getValue());
         }
         for (final DelegatingDriver driver : getEstimatedPropagationParameters().getDrivers()) {
             physicalEstimatedState.setEntry(i++, driver.getValue());
         }
         for (final DelegatingDriver driver : getEstimatedMeasurementsParameters().getDrivers()) {
             physicalEstimatedState.setEntry(i++, driver.getValue());
         }
 
         return physicalEstimatedState;
     }
 
     /** {@inheritDoc} */
     @Override
     public RealMatrix getPhysicalEstimatedCovarianceMatrix() {
         return correctedEstimate.getCovariance();
     }
 
     /** {@inheritDoc} */
     @Override
     public RealMatrix getPhysicalStateTransitionMatrix() {
         return null;
     }
 
     /** {@inheritDoc} */
     @Override
     public RealMatrix getPhysicalMeasurementJacobian() {
         return null;
     }
 
     /** {@inheritDoc} */
     @Override
     public RealMatrix getPhysicalInnovationCovarianceMatrix() {
         return correctedEstimate.getInnovationCovariance();
     }
 
     /** {@inheritDoc} */
     @Override
     public RealMatrix getPhysicalKalmanGain() {
         return correctedEstimate.getKalmanGain();
     }
 
     /** {@inheritDoc} */
     @Override
     public int getCurrentMeasurementNumber() {
         return currentMeasurementNumber;
     }
 
     /** {@inheritDoc} */
     @Override
     public AbsoluteDate getCurrentDate() {
         return currentDate;
     }
 
     /** {@inheritDoc} */
     @Override
     public EstimatedMeasurement<?> getPredictedMeasurement() {
         return predictedMeasurement;
     }
 
     /** {@inheritDoc} */
     @Override
     public EstimatedMeasurement<?> getCorrectedMeasurement() {
         return correctedMeasurement;
     }
 
     /** {@inheritDoc} */
     @Override
     public void updateNominalSpacecraftState(final SpacecraftState nominal) {
         this.nominalMeanSpacecraftState = nominal;
         // Short period terms
         shortPeriodicTerms = new ArrayRealVector(dsstPropagator.getShortPeriodTermsValue(nominalMeanSpacecraftState));
         // Update the builder with the nominal mean elements orbit
         builder.resetOrbit(nominal.getOrbit(), PropagationType.MEAN);
     }
 
     /** {@inheritDoc} */
     @Override
     public void updateShortPeriods(final SpacecraftState state) {
         // Loop on DSST force models
         for (final DSSTForceModel model : dsstPropagator.getAllForceModels()) {
             model.updateShortPeriodTerms(model.getParameters(), state);
         }
     }
 
     /** {@inheritDoc} */
     @Override
     public void initializeShortPeriodicTerms(final SpacecraftState meanState) {
         final List<ShortPeriodTerms> shortPeriodTerms = new ArrayList<>();
         for (final DSSTForceModel force :  builder.getAllForceModels()) {
             shortPeriodTerms.addAll(force.initializeShortPeriodTerms(new AuxiliaryElements(meanState.getOrbit(), 1), PropagationType.OSCULATING, force.getParameters()));
         }
         dsstPropagator.setShortPeriodTerms(shortPeriodTerms);
     }
 
     /** Compute the predicted osculating elements.
      * @param filterCorrection physical kalman filter correction
      * @param meanState mean spacecraft state
      * @param shortPeriodTerms short period terms for the given mean state
      * @return the predicted osculating element
      */
     private RealVector computeOsculatingElements(final RealVector filterCorrection,
                                                  final SpacecraftState meanState,
                                                  final RealVector shortPeriodTerms) {
 
         // Convert the input predicted mean state to a SpacecraftState
         final RealVector stateVector = toRealVector(meanState);
 
         // Return
         return stateVector.add(filterCorrection).add(shortPeriodTerms);
 
     }
 
     /** Convert a SpacecraftState to a RealVector.
      * @param state the input SpacecraftState
      * @return the corresponding RealVector
      */
     private RealVector toRealVector(final SpacecraftState state) {
 
         // Convert orbit to array
         final double[] stateArray = new double[6];
         orbitType.mapOrbitToArray(state.getOrbit(), angleType, stateArray, null);
 
         // Return the RealVector
         return new ArrayRealVector(stateArray);
 
     }
 
     /** Get the number of estimated orbital parameters.
      * @return the number of estimated orbital parameters
      */
     public int getNumberSelectedOrbitalDrivers() {
         return estimatedOrbitalParameters.getNbParams();
     }
 
     /** Get the number of estimated propagation parameters.
      * @return the number of estimated propagation parameters
      */
     public int getNumberSelectedPropagationDrivers() {
         return estimatedPropagationParameters.getNbParams();
     }
 
     /** Get the number of estimated measurement parameters.
      * @return the number of estimated measurement parameters
      */
     public int getNumberSelectedMeasurementDrivers() {
         return estimatedMeasurementsParameters.getNbParams();
     }
 
     /** Update the estimated parameters after the correction phase of the filter.
      * The min/max allowed values are handled by the parameter themselves.
      */
     private void updateParameters() {
         final RealVector correctedState = correctedEstimate.getState();
         int i = 0;
         for (final DelegatingDriver driver : getEstimatedOrbitalParameters().getDrivers()) {
             // let the parameter handle min/max clipping
             driver.setValue(driver.getValue() + correctedState.getEntry(i++));
         }
         for (final DelegatingDriver driver : getEstimatedPropagationParameters().getDrivers()) {
             // let the parameter handle min/max clipping
             driver.setValue(driver.getValue() + correctedState.getEntry(i++));
         }
         for (final DelegatingDriver driver : getEstimatedMeasurementsParameters().getDrivers()) {
             // let the parameter handle min/max clipping
             driver.setValue(driver.getValue() + correctedState.getEntry(i++));
         }
     }
 
 }