SemiAnalyticalKalmanEstimator.java
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package org.orekit.estimation.sequential;
import java.util.List;
import org.hipparchus.exception.MathRuntimeException;
import org.hipparchus.filtering.kalman.extended.ExtendedKalmanFilter;
import org.hipparchus.linear.MatrixDecomposer;
import org.hipparchus.linear.RealMatrix;
import org.hipparchus.linear.RealVector;
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.time.AbsoluteDate;
import org.orekit.utils.ParameterDriver;
import org.orekit.utils.ParameterDriversList;
import org.orekit.utils.ParameterDriversList.DelegatingDriver;
/**
* 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 {
/** Builders for orbit propagators. */
private DSSTPropagatorBuilder propagatorBuilder;
/** Kalman filter process model. */
private final SemiAnalyticalKalmanModel 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 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) {
this.propagatorBuilder = propagatorBuilder;
this.observer = null;
// 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());
}
/** Set the observer.
* @param observer the observer
*/
public void setObserver(final KalmanObserver observer) {
this.observer = observer;
}
/** Get the current measurement number.
* @return current measurement number
*/
public int getCurrentMeasurementNumber() {
return processModel.getCurrentMeasurementNumber();
}
/** Get the current date.
* @return current date
*/
public AbsoluteDate getCurrentDate() {
return processModel.getCurrentDate();
}
/** Get the "physical" estimated state (i.e. not normalized)
* @return the "physical" estimated state
*/
public RealVector getPhysicalEstimatedState() {
return processModel.getPhysicalEstimatedState();
}
/** Get the "physical" estimated covariance matrix (i.e. not normalized)
* @return the "physical" estimated covariance matrix
*/
public RealMatrix getPhysicalEstimatedCovarianceMatrix() {
return processModel.getPhysicalEstimatedCovarianceMatrix();
}
/** Get the orbital parameters supported by this estimator.
* <p>
* If there are more than one propagator builder, then the names
* of the drivers have an index marker in square brackets appended
* to them in order to distinguish the various orbits. So for example
* with one builder generating Keplerian orbits the names would be
* simply "a", "e", "i"... but if there are several builders the
* names would be "a[0]", "e[0]", "i[0]"..."a[1]", "e[1]", "i[1]"...
* </p>
* @param estimatedOnly if true, only estimated parameters are returned
* @return orbital parameters supported by this estimator
*/
public ParameterDriversList getOrbitalParametersDrivers(final boolean estimatedOnly) {
final ParameterDriversList estimated = new ParameterDriversList();
for (final ParameterDriver driver : propagatorBuilder.getOrbitalParametersDrivers().getDrivers()) {
if (driver.isSelected() || !estimatedOnly) {
driver.setName(driver.getName());
estimated.add(driver);
}
}
return estimated;
}
/** Get the propagator parameters supported by this estimator.
* @param estimatedOnly if true, only estimated parameters are returned
* @return propagator parameters supported by this estimator
*/
public ParameterDriversList getPropagationParametersDrivers(final boolean estimatedOnly) {
final ParameterDriversList estimated = new ParameterDriversList();
for (final DelegatingDriver delegating : propagatorBuilder.getPropagationParametersDrivers().getDrivers()) {
if (delegating.isSelected() || !estimatedOnly) {
for (final ParameterDriver driver : delegating.getRawDrivers()) {
estimated.add(driver);
}
}
}
return estimated;
}
/** Get the list of estimated measurements parameters.
* @return the list of estimated measurements parameters
*/
public ParameterDriversList getEstimatedMeasurementsParameters() {
return processModel.getEstimatedMeasurementsParameters();
}
/** 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 {
processModel.setObserver(observer);
return processModel.processMeasurements(observedMeasurements, filter);
} catch (MathRuntimeException mrte) {
throw new OrekitException(mrte);
}
}
}