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    *   http://www.apache.org/licenses/LICENSE-2.0
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   * Unless required by applicable law or agreed to in writing, software
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  package org.orekit.estimation.sequential;
  
  import java.util.Collections;
  import java.util.List;
  
  import org.hipparchus.exception.MathRuntimeException;
  import org.hipparchus.filtering.kalman.extended.ExtendedKalmanFilter;
  import org.hipparchus.linear.MatrixDecomposer;
  import org.orekit.errors.OrekitException;
  import org.orekit.estimation.measurements.ObservedMeasurement;
  import org.orekit.propagation.conversion.DSSTPropagatorBuilder;
  import org.orekit.propagation.semianalytical.dsst.DSSTPropagator;
  import org.orekit.utils.ParameterDriver;
  import org.orekit.utils.ParameterDriversList;
  
  /**
   * Implementation of an Extended Semi-analytical Kalman Filter (ESKF) to perform orbit determination.
   * <p>
   * The filter uses a {@link DSSTPropagatorBuilder}.
   * </p>
   * <p>
   * The estimated parameters are driven by {@link ParameterDriver} objects. They are of 3 different types:<ol>
   *   <li><b>Orbital parameters</b>:The position and velocity of the spacecraft, or, more generally, its orbit.<br>
   *       These parameters are retrieved from the reference trajectory propagator builder when the filter is initialized.</li>
   *   <li><b>Propagation parameters</b>: Some parameters modelling physical processes (SRP or drag coefficients).<br>
   *       They are also retrieved from the propagator builder during the initialization phase.</li>
   *   <li><b>Measurements parameters</b>: Parameters related to measurements (station biases, positions etc...).<br>
   *       They are passed down to the filter in its constructor.</li>
   * </ol>
   * <p>
   * The Kalman filter implementation used is provided by the underlying mathematical library Hipparchus.
   * All the variables seen by Hipparchus (states, covariances, measurement matrices...) are normalized
   * using a specific scale for each estimated parameters or standard deviation noise for each measurement components.
   * </p>
   *
   * @see "Folcik Z., Orbit Determination Using Modern Filters/Smoothers and Continuous Thrust Modeling,
   *       Master of Science Thesis, Department of Aeronautics and Astronautics, MIT, June, 2008."
   *
   * @see "Cazabonne B., Bayard J., Journot M., and Cefola P. J., A Semi-analytical Approach for Orbit
   *       Determination based on Extended Kalman Filter, AAS Paper 21-614, AAS/AIAA Astrodynamics
   *       Specialist Conference, Big Sky, August 2021."
   *
   * @author Julie Bayard
   * @author Bryan Cazabonne
   * @author Maxime Journot
   * @since 11.1
   */
  public class SemiAnalyticalKalmanEstimator extends AbstractKalmanEstimator {
  
      /** Kalman filter process model. */
      private final SemiAnalyticalKalmanModel processModel;
  
      /** Filter. */
      private final ExtendedKalmanFilter<MeasurementDecorator> filter;
  
      /** Kalman filter estimator constructor (package private).
       * @param decomposer decomposer to use for the correction phase
       * @param propagatorBuilder propagator builder used to evaluate the orbit.
       * @param covarianceMatrixProvider provider for process noise matrix
       * @param estimatedMeasurementParameters measurement parameters to estimate
       * @param measurementProcessNoiseMatrix provider for measurement process noise matrix
       */
      public SemiAnalyticalKalmanEstimator(final MatrixDecomposer decomposer,
                                           final DSSTPropagatorBuilder propagatorBuilder,
                                           final CovarianceMatrixProvider covarianceMatrixProvider,
                                           final ParameterDriversList estimatedMeasurementParameters,
                                           final CovarianceMatrixProvider measurementProcessNoiseMatrix) {
          super(Collections.singletonList(propagatorBuilder));
          // Build the process model and measurement model
          this.processModel = new SemiAnalyticalKalmanModel(propagatorBuilder, covarianceMatrixProvider,
                                                            estimatedMeasurementParameters,  measurementProcessNoiseMatrix);
  
          // Extended Kalman Filter of Hipparchus
          this.filter = new ExtendedKalmanFilter<>(decomposer, processModel, processModel.getEstimate());
  
      }
  
      /** {@inheritDoc}. */
      @Override
      protected KalmanEstimation getKalmanEstimation() {
          return processModel;
      }
  
     /** Set the observer.
      * @param observer the observer
      */
     public void setObserver(final KalmanObserver observer) {
         processModel.setObserver(observer);
     }
 
     /** Process a single measurement.
      * <p>
      * Update the filter with the new measurement by calling the estimate method.
      * </p>
      * @param observedMeasurements the list of measurements to process
      * @return estimated propagators
      */
     public DSSTPropagator processMeasurements(final List<ObservedMeasurement<?>> observedMeasurements) {
         try {
             return processModel.processMeasurements(observedMeasurements, filter);
         } catch (MathRuntimeException mrte) {
             throw new OrekitException(mrte);
         }
     }
 
 }