KalmanEstimator.java

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package org.orekit.estimation.sequential;

import java.util.List;

import org.hipparchus.exception.MathRuntimeException;
import org.hipparchus.filtering.kalman.ProcessEstimate;
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.Propagator;
import org.orekit.propagation.conversion.OrbitDeterminationPropagatorBuilder;
import org.orekit.propagation.numerical.NumericalPropagator;
import org.orekit.propagation.semianalytical.dsst.DSSTPropagator;
import org.orekit.time.AbsoluteDate;
import org.orekit.utils.ParameterDriver;
import org.orekit.utils.ParameterDriversList;


/**
 * Implementation of a Kalman filter to perform orbit determination.
 * <p>
 * The filter uses a {@link OrbitDeterminationPropagatorBuilder} to initialize its reference trajectory {@link NumericalPropagator}
 * or {@link DSSTPropagator}.
 * </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 etc...).<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 total number of estimated parameters is m, the size of the state vector.
 * </p>
 * <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>
 *
 * <p>A {@link KalmanEstimator} object is built using the {@link KalmanEstimatorBuilder#build() build}
 * method of a {@link KalmanEstimatorBuilder}.</p>
 *
 * @author Romain Gerbaud
 * @author Maxime Journot
 * @author Luc Maisonobe
 * @since 9.2
 */
public class KalmanEstimator extends AbstractKalmanEstimator {

    /** Reference date. */
    private final AbsoluteDate referenceDate;

    /** Kalman filter process model. */
    private final AbstractKalmanModel processModel;

    /** Filter. */
    private final ExtendedKalmanFilter<MeasurementDecorator> filter;

    /** Observer to retrieve current estimation info. */
    private KalmanObserver observer;

    /** Kalman filter estimator constructor (package private).
     * @param decomposer decomposer to use for the correction phase
     * @param propagatorBuilders propagators builders used to evaluate the orbit.
     * @param processNoiseMatricesProviders providers for process noise matrices
     * @param estimatedMeasurementParameters measurement parameters to estimate
     * @param measurementProcessNoiseMatrix provider for measurement process noise matrix
     * @since 10.3
     */
    KalmanEstimator(final MatrixDecomposer decomposer,
                    final List<OrbitDeterminationPropagatorBuilder> propagatorBuilders,
                    final List<CovarianceMatrixProvider> processNoiseMatricesProviders,
                    final ParameterDriversList estimatedMeasurementParameters,
                    final CovarianceMatrixProvider measurementProcessNoiseMatrix) {
        super(propagatorBuilders);
        this.referenceDate      = propagatorBuilders.get(0).getInitialOrbitDate();
        this.observer           = null;

        // Build the process model and measurement model
        this.processModel = propagatorBuilders.get(0).buildKalmanModel(propagatorBuilders,
                                                                       processNoiseMatricesProviders,
                                                                       estimatedMeasurementParameters,
                                                                       measurementProcessNoiseMatrix);

        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) {
        this.observer = observer;
    }

    /** Process a single measurement.
     * <p>
     * Update the filter with the new measurement by calling the estimate method.
     * </p>
     * @param observedMeasurement the measurement to process
     * @return estimated propagators
     */
    public Propagator[] estimationStep(final ObservedMeasurement<?> observedMeasurement) {
        try {
            final ProcessEstimate estimate = filter.estimationStep(KalmanEstimatorUtil.decorate(observedMeasurement, referenceDate));
            processModel.finalizeEstimation(observedMeasurement, estimate);
            if (observer != null) {
                observer.evaluationPerformed(processModel);
            }
            return processModel.getEstimatedPropagators();
        } catch (MathRuntimeException mrte) {
            throw new OrekitException(mrte);
        }
    }

    /** Process several measurements.
     * @param observedMeasurements the measurements to process in <em>chronologically sorted</em> order
     * @return estimated propagators
     */
    public Propagator[] processMeasurements(final Iterable<ObservedMeasurement<?>> observedMeasurements) {
        Propagator[] propagators = null;
        for (ObservedMeasurement<?> observedMeasurement : observedMeasurements) {
            propagators = estimationStep(observedMeasurement);
        }
        return propagators;
    }

}