SemiAnalyticalKalmanEstimator.java
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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);
}
}
}