SemiAnalyticalUnscentedKalmanEstimator.java

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package org.orekit.estimation.sequential;

import java.util.Arrays;
import java.util.Collections;
import java.util.List;

import org.hipparchus.filtering.kalman.KalmanFilter;
import org.hipparchus.filtering.kalman.unscented.UnscentedKalmanFilter;
import org.hipparchus.linear.MatrixDecomposer;
import org.hipparchus.util.UnscentedTransformProvider;
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 Unscented Semi-analytical Kalman filter (USKF) 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 modeling 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 Kalman filter implementation used is provided by the underlying mathematical library Hipparchus.
 * All the variables seen by Hipparchus (states, covariances...) are normalized
 * using a specific scale for each estimated parameters or standard deviation noise for each measurement components.
 * </p>
 *
 * <p>An {@link SemiAnalyticalUnscentedKalmanEstimator} object is built using the {@link SemiAnalyticalUnscentedKalmanEstimatorBuilder#build() build}
 * method of a {@link SemiAnalyticalUnscentedKalmanEstimatorBuilder}.</p>
 *
 * @author Gaëtan Pierre
 * @author Bryan Cazabonne
 * @since 11.3
 */
public class SemiAnalyticalUnscentedKalmanEstimator extends AbstractKalmanEstimator {

    /** Unscented Kalman filter process model. */
    private final SemiAnalyticalUnscentedKalmanModel processModel;

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

    /** Dummy scale. */
    private final double[] scale;

    /** Unscented 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 processNoiseMatricesProvider provider for process noise matrix
     * @param estimatedMeasurementParameters measurement parameters to estimate
     * @param measurementProcessNoiseMatrix provider for measurement process noise matrix
     * @param utProvider provider for the unscented transform
     */
    SemiAnalyticalUnscentedKalmanEstimator(final MatrixDecomposer decomposer,
                                           final DSSTPropagatorBuilder propagatorBuilder,
                                           final CovarianceMatrixProvider processNoiseMatricesProvider,
                                           final ParameterDriversList estimatedMeasurementParameters,
                                           final CovarianceMatrixProvider measurementProcessNoiseMatrix,
                                           final UnscentedTransformProvider utProvider) {
        super(decomposer, Collections.singletonList(propagatorBuilder));
        // Build the process model and measurement model
        this.processModel = new SemiAnalyticalUnscentedKalmanModel(propagatorBuilder, processNoiseMatricesProvider,
                                                                   estimatedMeasurementParameters, measurementProcessNoiseMatrix);

        // Unscented Kalman Filter of Hipparchus
        this.filter = new UnscentedKalmanFilter<>(decomposer, processModel, processModel.getEstimate(), utProvider);

        // Fill dummy scale with 1s
        final int dim = processModel.getEstimate().getState().getDimension();
        this.scale = new double[dim];
        Arrays.fill(scale, 1.0);

    }

    /** {@inheritDoc}. */
    @Override
    protected KalmanEstimation getKalmanEstimation() {
        return processModel;
    }

    /** {@inheritDoc}. */
    @Override
    protected KalmanFilter<MeasurementDecorator> getKalmanFilter() {
        return filter;
    }

    /** {@inheritDoc}. */
    @Override
    protected double[] getScale() {
        return scale;
    }

    /** {@inheritDoc}. */
    @Override
    public void setObserver(final KalmanObserver observer) {
        processModel.setObserver(observer);
        observer.init(getKalmanEstimation());
    }

    /** {@inheritDoc}. */
    @Override
    public KalmanObserver getObserver() {
        return processModel.getObserver();
    }

    /** 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) {
        return processModel.processMeasurements(observedMeasurements, filter);
    }

}