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.analytical.BrouwerLyddanePropagator;
import org.orekit.propagation.analytical.EcksteinHechlerPropagator;
import org.orekit.propagation.analytical.Ephemeris;
import org.orekit.propagation.analytical.KeplerianPropagator;
import org.orekit.propagation.analytical.tle.TLEPropagator;
import org.orekit.propagation.conversion.PropagatorBuilder;
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 PropagatorBuilder} to initialize its reference trajectory.
* The Kalman estimator can be used with a {@link NumericalPropagator}, {@link TLEPropagator},
* {@link BrouwerLyddanePropagator}, {@link EcksteinHechlerPropagator}, {@link KeplerianPropagator},
* or {@link Ephemeris}.
* </p>
* <p>
* Kalman estimation using a {@link DSSTPropagator semi-analytical orbit propagator} must be done using
* the {@link SemiAnalyticalKalmanEstimator}.
* </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 KalmanModel 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<PropagatorBuilder> 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 = new KalmanModel(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;
}
}