KalmanEstimatorBuilder.java
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package org.orekit.estimation.sequential;
import java.util.ArrayList;
import java.util.List;
import org.hipparchus.linear.MatrixDecomposer;
import org.hipparchus.linear.QRDecomposer;
import org.orekit.errors.OrekitException;
import org.orekit.errors.OrekitMessages;
import org.orekit.propagation.conversion.EphemerisPropagatorBuilder;
import org.orekit.propagation.conversion.PropagatorBuilder;
import org.orekit.utils.ParameterDriversList;
/** Builder for a Kalman filter estimator.
* @author Romain Gerbaud
* @author Maxime Journot
* @since 9.2
*/
public class KalmanEstimatorBuilder {
/** Decomposer to use for the correction phase. */
private MatrixDecomposer decomposer;
/** Builders for propagators. */
private List<PropagatorBuilder> propagatorBuilders;
/** Estimated measurements parameters. */
private ParameterDriversList estimatedMeasurementsParameters;
/** Process noise matrices providers. */
private List<CovarianceMatrixProvider> processNoiseMatricesProviders;
/** Process noise matrix provider for measurement parameters. */
private CovarianceMatrixProvider measurementProcessNoiseMatrix;
/** Default constructor.
* Set an extended Kalman filter, with linearized covariance prediction.
*/
public KalmanEstimatorBuilder() {
this.decomposer = new QRDecomposer(1.0e-15);
this.propagatorBuilders = new ArrayList<>();
this.estimatedMeasurementsParameters = new ParameterDriversList();
this.processNoiseMatricesProviders = new ArrayList<>();
this.measurementProcessNoiseMatrix = null;
}
/** Construct a {@link KalmanEstimator} from the data in this builder.
* <p>
* Before this method is called, {@link #addPropagationConfiguration(PropagatorBuilder,
* CovarianceMatrixProvider) addPropagationConfiguration()} must have been called
* at least once, otherwise configuration is incomplete and an exception will be raised.
* </p>
* @return a new {@link KalmanEstimator}.
*/
public KalmanEstimator build() {
final int n = propagatorBuilders.size();
if (n == 0) {
throw new OrekitException(OrekitMessages.NO_PROPAGATOR_CONFIGURED);
}
return new KalmanEstimator(decomposer, propagatorBuilders, processNoiseMatricesProviders,
estimatedMeasurementsParameters, measurementProcessNoiseMatrix);
}
/** Configure the matrix decomposer.
* @param matrixDecomposer decomposer to use for the correction phase
* @return this object.
*/
public KalmanEstimatorBuilder decomposer(final MatrixDecomposer matrixDecomposer) {
decomposer = matrixDecomposer;
return this;
}
/** Add a propagation configuration.
* <p>
* This method must be called once for each propagator to managed with the
* {@link KalmanEstimator Kalman estimator}. The propagators order in the
* Kalman filter will be the call order.
* </p>
* <p>
* The {@code provider} should return a matrix with dimensions and ordering
* consistent with the {@code builder} configuration. The first 6 rows/columns
* correspond to the 6 orbital parameters. The remaining elements correspond
* to the subset of propagation parameters that are estimated, in the
* same order as propagatorBuilder.{@link
* org.orekit.propagation.conversion.PropagatorBuilder#getPropagationParametersDrivers()
* getPropagationParametersDrivers()}.{@link org.orekit.utils.ParameterDriversList#getDrivers()
* getDrivers()} (but filtering out the non selected drivers).
* </p>
* @param builder The propagator builder to use in the Kalman filter.
* @param provider The process noise matrices provider to use, consistent with the builder.
* This parameter can be equal to {@code null} if the input builder is
* an {@link EphemerisPropagatorBuilder}. Indeed, for ephemeris based estimation
* only measurement parameters are estimated. Therefore, the covariance related
* to dynamical parameters can be null.
* @return this object.
* @see CovarianceMatrixProvider#getProcessNoiseMatrix(org.orekit.propagation.SpacecraftState,
* org.orekit.propagation.SpacecraftState) getProcessNoiseMatrix(previous, current)
*/
public KalmanEstimatorBuilder addPropagationConfiguration(final PropagatorBuilder builder,
final CovarianceMatrixProvider provider) {
propagatorBuilders.add(builder);
processNoiseMatricesProviders.add(provider);
return this;
}
/** Configure the estimated measurement parameters.
* <p>
* If this method is not called, no measurement parameters will be estimated.
* </p>
* @param estimatedMeasurementsParams The estimated measurements' parameters list.
* @param provider covariance matrix provider for the estimated measurement parameters
* @return this object.
* @since 10.3
*/
public KalmanEstimatorBuilder estimatedMeasurementsParameters(final ParameterDriversList estimatedMeasurementsParams,
final CovarianceMatrixProvider provider) {
estimatedMeasurementsParameters = estimatedMeasurementsParams;
measurementProcessNoiseMatrix = provider;
return this;
}
}