KalmanModel.java

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 * 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
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 *   http://www.apache.org/licenses/LICENSE-2.0
 *
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package org.orekit.estimation.sequential;

import java.util.List;

import org.orekit.propagation.PropagationType;
import org.orekit.propagation.Propagator;
import org.orekit.propagation.SpacecraftState;
import org.orekit.propagation.conversion.OrbitDeterminationPropagatorBuilder;
import org.orekit.propagation.integration.AbstractJacobiansMapper;
import org.orekit.propagation.numerical.JacobiansMapper;
import org.orekit.propagation.numerical.NumericalPropagator;
import org.orekit.propagation.numerical.PartialDerivativesEquations;
import org.orekit.utils.ParameterDriversList;

/** Class defining the process model dynamics to use with a {@link KalmanEstimator}.
 * @author Romain Gerbaud
 * @author Maxime Journot
 * @since 9.2
 */
public class KalmanModel extends AbstractKalmanModel {

    /** Kalman process model constructor.
     * @param propagatorBuilders propagators builders used to evaluate the orbits.
     * @param covarianceMatricesProviders providers for covariance matrices
     * @param estimatedMeasurementParameters measurement parameters to estimate
     * @param measurementProcessNoiseMatrix provider for measurement process noise matrix
     */
    public KalmanModel(final List<OrbitDeterminationPropagatorBuilder> propagatorBuilders,
                       final List<CovarianceMatrixProvider> covarianceMatricesProviders,
                       final ParameterDriversList estimatedMeasurementParameters,
                       final CovarianceMatrixProvider measurementProcessNoiseMatrix) {
        // call super constructor
        super(propagatorBuilders, covarianceMatricesProviders, estimatedMeasurementParameters,
              measurementProcessNoiseMatrix, new JacobiansMapper[propagatorBuilders.size()]);
    }

    /** {@inheritDoc} */
    @Override
    protected void updateReferenceTrajectories(final Propagator[] propagators,
                                               final PropagationType pType,
                                               final PropagationType sType) {

        // Update the reference trajectory propagator
        setReferenceTrajectories(propagators);

        // Jacobian mappers
        final AbstractJacobiansMapper[] mappers = getMappers();

        for (int k = 0; k < propagators.length; ++k) {
            // Link the partial derivatives to this new propagator
            final String equationName = KalmanEstimator.class.getName() + "-derivatives-" + k;
            final PartialDerivativesEquations pde = new PartialDerivativesEquations(equationName, (NumericalPropagator) getReferenceTrajectories()[k]);

            // Reset the Jacobians
            final SpacecraftState rawState = getReferenceTrajectories()[k].getInitialState();
            final SpacecraftState stateWithDerivatives = pde.setInitialJacobians(rawState);
            getReferenceTrajectories()[k].resetInitialState(stateWithDerivatives);
            mappers[k] = pde.getMapper();
        }

        // Update Jacobian mappers
        setMappers(mappers);

    }

    /** {@inheritDoc} */
    @Override
    protected void analyticalDerivativeComputations(final AbstractJacobiansMapper mapper, final SpacecraftState state) {
        // does nothing
        // numerical method does not require analytical terms calculations
    }

}