SemiAnalyticalKalmanModel.java
/* Copyright 2002-2024 CS GROUP
* Licensed to CS GROUP (CS) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* CS licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.orekit.estimation.sequential;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.hipparchus.exception.MathRuntimeException;
import org.hipparchus.filtering.kalman.ProcessEstimate;
import org.hipparchus.filtering.kalman.extended.ExtendedKalmanFilter;
import org.hipparchus.filtering.kalman.extended.NonLinearEvolution;
import org.hipparchus.filtering.kalman.extended.NonLinearProcess;
import org.hipparchus.linear.Array2DRowRealMatrix;
import org.hipparchus.linear.ArrayRealVector;
import org.hipparchus.linear.MatrixUtils;
import org.hipparchus.linear.QRDecomposition;
import org.hipparchus.linear.RealMatrix;
import org.hipparchus.linear.RealVector;
import org.hipparchus.util.FastMath;
import org.orekit.errors.OrekitException;
import org.orekit.estimation.measurements.EstimatedMeasurement;
import org.orekit.estimation.measurements.ObservedMeasurement;
import org.orekit.orbits.Orbit;
import org.orekit.orbits.OrbitType;
import org.orekit.propagation.PropagationType;
import org.orekit.propagation.SpacecraftState;
import org.orekit.propagation.conversion.DSSTPropagatorBuilder;
import org.orekit.propagation.semianalytical.dsst.DSSTHarvester;
import org.orekit.propagation.semianalytical.dsst.DSSTPropagator;
import org.orekit.propagation.semianalytical.dsst.forces.DSSTForceModel;
import org.orekit.propagation.semianalytical.dsst.forces.ShortPeriodTerms;
import org.orekit.propagation.semianalytical.dsst.utilities.AuxiliaryElements;
import org.orekit.time.AbsoluteDate;
import org.orekit.time.ChronologicalComparator;
import org.orekit.utils.ParameterDriver;
import org.orekit.utils.ParameterDriversList;
import org.orekit.utils.ParameterDriversList.DelegatingDriver;
import org.orekit.utils.TimeSpanMap.Span;
/** Process model to use with a {@link SemiAnalyticalKalmanEstimator}.
*
* @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 SemiAnalyticalKalmanModel implements KalmanEstimation, NonLinearProcess<MeasurementDecorator>, SemiAnalyticalProcess {
/** Builders for DSST propagator. */
private final DSSTPropagatorBuilder builder;
/** Estimated orbital parameters. */
private final ParameterDriversList estimatedOrbitalParameters;
/** Per-builder estimated propagation drivers. */
private final ParameterDriversList estimatedPropagationParameters;
/** Estimated measurements parameters. */
private final ParameterDriversList estimatedMeasurementsParameters;
/** Map for propagation parameters columns. */
private final Map<String, Integer> propagationParameterColumns;
/** Map for measurements parameters columns. */
private final Map<String, Integer> measurementParameterColumns;
/** Scaling factors. */
private final double[] scale;
/** Provider for covariance matrix. */
private final CovarianceMatrixProvider covarianceMatrixProvider;
/** Process noise matrix provider for measurement parameters. */
private final CovarianceMatrixProvider measurementProcessNoiseMatrix;
/** Harvester between two-dimensional Jacobian matrices and one-dimensional additional state arrays. */
private DSSTHarvester harvester;
/** Propagators for the reference trajectories, up to current date. */
private DSSTPropagator dsstPropagator;
/** Observer to retrieve current estimation info. */
private KalmanObserver observer;
/** Current number of measurement. */
private int currentMeasurementNumber;
/** Current date. */
private AbsoluteDate currentDate;
/** Predicted mean element filter correction. */
private RealVector predictedFilterCorrection;
/** Corrected mean element filter correction. */
private RealVector correctedFilterCorrection;
/** Predicted measurement. */
private EstimatedMeasurement<?> predictedMeasurement;
/** Corrected measurement. */
private EstimatedMeasurement<?> correctedMeasurement;
/** Nominal mean spacecraft state. */
private SpacecraftState nominalMeanSpacecraftState;
/** Previous nominal mean spacecraft state. */
private SpacecraftState previousNominalMeanSpacecraftState;
/** Current corrected estimate. */
private ProcessEstimate correctedEstimate;
/** Inverse of the orbital part of the state transition matrix. */
private RealMatrix phiS;
/** Propagation parameters part of the state transition matrix. */
private RealMatrix psiS;
/** Kalman process model constructor (package private).
* @param propagatorBuilder propagators builders used to evaluate the orbits.
* @param covarianceMatrixProvider provider for covariance matrix
* @param estimatedMeasurementParameters measurement parameters to estimate
* @param measurementProcessNoiseMatrix provider for measurement process noise matrix
*/
protected SemiAnalyticalKalmanModel(final DSSTPropagatorBuilder propagatorBuilder,
final CovarianceMatrixProvider covarianceMatrixProvider,
final ParameterDriversList estimatedMeasurementParameters,
final CovarianceMatrixProvider measurementProcessNoiseMatrix) {
this.builder = propagatorBuilder;
this.estimatedMeasurementsParameters = estimatedMeasurementParameters;
this.measurementParameterColumns = new HashMap<>(estimatedMeasurementsParameters.getDrivers().size());
this.observer = null;
this.currentMeasurementNumber = 0;
this.currentDate = propagatorBuilder.getInitialOrbitDate();
this.covarianceMatrixProvider = covarianceMatrixProvider;
this.measurementProcessNoiseMatrix = measurementProcessNoiseMatrix;
// Number of estimated parameters
int columns = 0;
// Set estimated orbital parameters
estimatedOrbitalParameters = new ParameterDriversList();
for (final ParameterDriver driver : builder.getOrbitalParametersDrivers().getDrivers()) {
// Verify if the driver reference date has been set
if (driver.getReferenceDate() == null) {
driver.setReferenceDate(currentDate);
}
// Verify if the driver is selected
if (driver.isSelected()) {
estimatedOrbitalParameters.add(driver);
columns++;
}
}
// Set estimated propagation parameters
estimatedPropagationParameters = new ParameterDriversList();
final List<String> estimatedPropagationParametersNames = new ArrayList<>();
for (final ParameterDriver driver : builder.getPropagationParametersDrivers().getDrivers()) {
// Verify if the driver reference date has been set
if (driver.getReferenceDate() == null) {
driver.setReferenceDate(currentDate);
}
// Verify if the driver is selected
if (driver.isSelected()) {
estimatedPropagationParameters.add(driver);
// Add the driver name if it has not been added yet
for (Span<String> span = driver.getNamesSpanMap().getFirstSpan(); span != null; span = span.next()) {
if (!estimatedPropagationParametersNames.contains(span.getData())) {
estimatedPropagationParametersNames.add(span.getData());
}
}
}
}
estimatedPropagationParametersNames.sort(Comparator.naturalOrder());
// Populate the map of propagation drivers' columns and update the total number of columns
propagationParameterColumns = new HashMap<>(estimatedPropagationParametersNames.size());
for (final String driverName : estimatedPropagationParametersNames) {
propagationParameterColumns.put(driverName, columns);
++columns;
}
// Set the estimated measurement parameters
for (final ParameterDriver parameter : estimatedMeasurementsParameters.getDrivers()) {
if (parameter.getReferenceDate() == null) {
parameter.setReferenceDate(currentDate);
}
for (Span<String> span = parameter.getNamesSpanMap().getFirstSpan(); span != null; span = span.next()) {
measurementParameterColumns.put(span.getData(), columns);
++columns;
}
}
// Compute the scale factors
this.scale = new double[columns];
int index = 0;
for (final ParameterDriver driver : estimatedOrbitalParameters.getDrivers()) {
scale[index++] = driver.getScale();
}
for (final ParameterDriver driver : estimatedPropagationParameters.getDrivers()) {
for (Span<String> span = driver.getNamesSpanMap().getFirstSpan(); span != null; span = span.next()) {
scale[index++] = driver.getScale();
}
}
for (final ParameterDriver driver : estimatedMeasurementsParameters.getDrivers()) {
for (Span<String> span = driver.getNamesSpanMap().getFirstSpan(); span != null; span = span.next()) {
scale[index++] = driver.getScale();
}
}
// Build the reference propagator and add its partial derivatives equations implementation
updateReferenceTrajectory(getEstimatedPropagator());
this.nominalMeanSpacecraftState = dsstPropagator.getInitialState();
this.previousNominalMeanSpacecraftState = nominalMeanSpacecraftState;
// Initialize "field" short periodic terms
harvester.initializeFieldShortPeriodTerms(nominalMeanSpacecraftState);
// Initialize the estimated normalized mean element filter correction (See Ref [1], Eq. 3.2a)
this.predictedFilterCorrection = MatrixUtils.createRealVector(columns);
this.correctedFilterCorrection = predictedFilterCorrection;
// Initialize propagation parameters part of the state transition matrix (See Ref [1], Eq. 3.2c)
this.psiS = null;
if (estimatedPropagationParameters.getNbParams() != 0) {
this.psiS = MatrixUtils.createRealMatrix(getNumberSelectedOrbitalDriversValuesToEstimate(),
getNumberSelectedPropagationDriversValuesToEstimate());
}
// Initialize inverse of the orbital part of the state transition matrix (See Ref [1], Eq. 3.2d)
this.phiS = MatrixUtils.createRealIdentityMatrix(getNumberSelectedOrbitalDriversValuesToEstimate());
// Number of estimated measurement parameters
final int nbMeas = getNumberSelectedMeasurementDriversValuesToEstimate();
// Number of estimated dynamic parameters (orbital + propagation)
final int nbDyn = getNumberSelectedOrbitalDriversValuesToEstimate() + getNumberSelectedPropagationDriversValuesToEstimate();
// Covariance matrix
final RealMatrix noiseK = MatrixUtils.createRealMatrix(nbDyn + nbMeas, nbDyn + nbMeas);
final RealMatrix noiseP = covarianceMatrixProvider.getInitialCovarianceMatrix(nominalMeanSpacecraftState);
noiseK.setSubMatrix(noiseP.getData(), 0, 0);
if (measurementProcessNoiseMatrix != null) {
final RealMatrix noiseM = measurementProcessNoiseMatrix.getInitialCovarianceMatrix(nominalMeanSpacecraftState);
noiseK.setSubMatrix(noiseM.getData(), nbDyn, nbDyn);
}
// Verify dimension
KalmanEstimatorUtil.checkDimension(noiseK.getRowDimension(),
builder.getOrbitalParametersDrivers(),
builder.getPropagationParametersDrivers(),
estimatedMeasurementsParameters);
final RealMatrix correctedCovariance = KalmanEstimatorUtil.normalizeCovarianceMatrix(noiseK, scale);
// Initialize corrected estimate
this.correctedEstimate = new ProcessEstimate(0.0, correctedFilterCorrection, correctedCovariance);
}
/** {@inheritDoc} */
@Override
public KalmanObserver getObserver() {
return observer;
}
/** Set the observer.
* @param observer the observer
*/
public void setObserver(final KalmanObserver observer) {
this.observer = observer;
}
/** Get the current corrected estimate.
* @return current corrected estimate
*/
public ProcessEstimate getEstimate() {
return correctedEstimate;
}
/** Process a single measurement.
* <p>
* Update the filter with the new measurements.
* </p>
* @param observedMeasurements the list of measurements to process
* @param filter Extended Kalman Filter
* @return estimated propagator
*/
public DSSTPropagator processMeasurements(final List<ObservedMeasurement<?>> observedMeasurements,
final ExtendedKalmanFilter<MeasurementDecorator> filter) {
try {
// Sort the measurement
observedMeasurements.sort(new ChronologicalComparator());
final AbsoluteDate tStart = observedMeasurements.get(0).getDate();
final AbsoluteDate tEnd = observedMeasurements.get(observedMeasurements.size() - 1).getDate();
final double overshootTimeRange = FastMath.nextAfter(tEnd.durationFrom(tStart),
Double.POSITIVE_INFINITY);
// Initialize step handler and set it to the propagator
final SemiAnalyticalMeasurementHandler stepHandler = new SemiAnalyticalMeasurementHandler(this, filter, observedMeasurements, builder.getInitialOrbitDate());
dsstPropagator.getMultiplexer().add(stepHandler);
dsstPropagator.propagate(tStart, tStart.shiftedBy(overshootTimeRange));
// Return the last estimated propagator
return getEstimatedPropagator();
} catch (MathRuntimeException mrte) {
throw new OrekitException(mrte);
}
}
/** Get the propagator estimated with the values set in the propagator builder.
* @return propagator based on the current values in the builder
*/
public DSSTPropagator getEstimatedPropagator() {
// Return propagator built with current instantiation of the propagator builder
return builder.buildPropagator(builder.getSelectedNormalizedParameters());
}
/** {@inheritDoc} */
@Override
public NonLinearEvolution getEvolution(final double previousTime, final RealVector previousState,
final MeasurementDecorator measurement) {
// Set a reference date for all measurements parameters that lack one (including the not estimated ones)
final ObservedMeasurement<?> observedMeasurement = measurement.getObservedMeasurement();
for (final ParameterDriver driver : observedMeasurement.getParametersDrivers()) {
if (driver.getReferenceDate() == null) {
driver.setReferenceDate(builder.getInitialOrbitDate());
}
}
// Increment measurement number
++currentMeasurementNumber;
// Update the current date
currentDate = measurement.getObservedMeasurement().getDate();
// Normalized state transition matrix
final RealMatrix stm = getErrorStateTransitionMatrix();
// Predict filter correction
predictedFilterCorrection = predictFilterCorrection(stm);
// Short period term derivatives
analyticalDerivativeComputations(nominalMeanSpacecraftState);
// Calculate the predicted osculating elements
final double[] osculating = computeOsculatingElements(predictedFilterCorrection);
final Orbit osculatingOrbit = OrbitType.EQUINOCTIAL.mapArrayToOrbit(osculating, null, builder.getPositionAngleType(),
currentDate, nominalMeanSpacecraftState.getMu(),
nominalMeanSpacecraftState.getFrame());
// Compute the predicted measurements (See Ref [1], Eq. 3.8)
predictedMeasurement = observedMeasurement.estimate(currentMeasurementNumber,
currentMeasurementNumber,
new SpacecraftState[] {
new SpacecraftState(osculatingOrbit,
nominalMeanSpacecraftState.getAttitude(),
nominalMeanSpacecraftState.getMass(),
nominalMeanSpacecraftState.getAdditionalStatesValues(),
nominalMeanSpacecraftState.getAdditionalStatesDerivatives())
});
// Normalized measurement matrix
final RealMatrix measurementMatrix = getMeasurementMatrix();
// Number of estimated measurement parameters
final int nbMeas = getNumberSelectedMeasurementDriversValuesToEstimate();
// Number of estimated dynamic parameters (orbital + propagation)
final int nbDyn = getNumberSelectedOrbitalDriversValuesToEstimate() + getNumberSelectedPropagationDriversValuesToEstimate();
// Covariance matrix
final RealMatrix noiseK = MatrixUtils.createRealMatrix(nbDyn + nbMeas, nbDyn + nbMeas);
final RealMatrix noiseP = covarianceMatrixProvider.getProcessNoiseMatrix(previousNominalMeanSpacecraftState, nominalMeanSpacecraftState);
noiseK.setSubMatrix(noiseP.getData(), 0, 0);
if (measurementProcessNoiseMatrix != null) {
final RealMatrix noiseM = measurementProcessNoiseMatrix.getProcessNoiseMatrix(previousNominalMeanSpacecraftState, nominalMeanSpacecraftState);
noiseK.setSubMatrix(noiseM.getData(), nbDyn, nbDyn);
}
// Verify dimension
KalmanEstimatorUtil.checkDimension(noiseK.getRowDimension(),
builder.getOrbitalParametersDrivers(),
builder.getPropagationParametersDrivers(),
estimatedMeasurementsParameters);
final RealMatrix normalizedProcessNoise = KalmanEstimatorUtil.normalizeCovarianceMatrix(noiseK, scale);
// Return
return new NonLinearEvolution(measurement.getTime(), predictedFilterCorrection, stm,
normalizedProcessNoise, measurementMatrix);
}
/** {@inheritDoc} */
@Override
public RealVector getInnovation(final MeasurementDecorator measurement, final NonLinearEvolution evolution,
final RealMatrix innovationCovarianceMatrix) {
// Apply the dynamic outlier filter, if it exists
KalmanEstimatorUtil.applyDynamicOutlierFilter(predictedMeasurement, innovationCovarianceMatrix);
// Compute the innovation vector
return KalmanEstimatorUtil.computeInnovationVector(predictedMeasurement, predictedMeasurement.getObservedMeasurement().getTheoreticalStandardDeviation());
}
/** {@inheritDoc} */
@Override
public void finalizeEstimation(final ObservedMeasurement<?> observedMeasurement,
final ProcessEstimate estimate) {
// Update the process estimate
correctedEstimate = estimate;
// Corrected filter correction
correctedFilterCorrection = estimate.getState();
// Update the previous nominal mean spacecraft state
previousNominalMeanSpacecraftState = nominalMeanSpacecraftState;
// Calculate the corrected osculating elements
final double[] osculating = computeOsculatingElements(correctedFilterCorrection);
final Orbit osculatingOrbit = OrbitType.EQUINOCTIAL.mapArrayToOrbit(osculating, null, builder.getPositionAngleType(),
currentDate, nominalMeanSpacecraftState.getMu(),
nominalMeanSpacecraftState.getFrame());
// Compute the corrected measurements
correctedMeasurement = observedMeasurement.estimate(currentMeasurementNumber,
currentMeasurementNumber,
new SpacecraftState[] {
new SpacecraftState(osculatingOrbit,
nominalMeanSpacecraftState.getAttitude(),
nominalMeanSpacecraftState.getMass(),
nominalMeanSpacecraftState.getAdditionalStatesValues(),
nominalMeanSpacecraftState.getAdditionalStatesDerivatives())
});
// Call the observer if the user add one
if (observer != null) {
observer.evaluationPerformed(this);
}
}
/** {@inheritDoc} */
@Override
public void finalizeOperationsObservationGrid() {
// Update parameters
updateParameters();
}
/** {@inheritDoc} */
@Override
public ParameterDriversList getEstimatedOrbitalParameters() {
return estimatedOrbitalParameters;
}
/** {@inheritDoc} */
@Override
public ParameterDriversList getEstimatedPropagationParameters() {
return estimatedPropagationParameters;
}
/** {@inheritDoc} */
@Override
public ParameterDriversList getEstimatedMeasurementsParameters() {
return estimatedMeasurementsParameters;
}
/** {@inheritDoc} */
@Override
public SpacecraftState[] getPredictedSpacecraftStates() {
return new SpacecraftState[] {nominalMeanSpacecraftState};
}
/** {@inheritDoc} */
@Override
public SpacecraftState[] getCorrectedSpacecraftStates() {
return new SpacecraftState[] {getEstimatedPropagator().getInitialState()};
}
/** {@inheritDoc} */
@Override
public RealVector getPhysicalEstimatedState() {
// Method {@link ParameterDriver#getValue()} is used to get
// the physical values of the state.
// The scales'array is used to get the size of the state vector
final RealVector physicalEstimatedState = new ArrayRealVector(scale.length);
int i = 0;
for (final DelegatingDriver driver : getEstimatedOrbitalParameters().getDrivers()) {
for (Span<Double> span = driver.getValueSpanMap().getFirstSpan(); span != null; span = span.next()) {
physicalEstimatedState.setEntry(i++, span.getData());
}
}
for (final DelegatingDriver driver : getEstimatedPropagationParameters().getDrivers()) {
for (Span<Double> span = driver.getValueSpanMap().getFirstSpan(); span != null; span = span.next()) {
physicalEstimatedState.setEntry(i++, span.getData());
}
}
for (final DelegatingDriver driver : getEstimatedMeasurementsParameters().getDrivers()) {
for (Span<Double> span = driver.getValueSpanMap().getFirstSpan(); span != null; span = span.next()) {
physicalEstimatedState.setEntry(i++, span.getData());
}
}
return physicalEstimatedState;
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalEstimatedCovarianceMatrix() {
// Un-normalize the estimated covariance matrix (P) from Hipparchus and return it.
// The covariance P is an mxm matrix where m = nbOrb + nbPropag + nbMeas
// For each element [i,j] of P the corresponding normalized value is:
// Pn[i,j] = P[i,j] / (scale[i]*scale[j])
// Consequently: P[i,j] = Pn[i,j] * scale[i] * scale[j]
return KalmanEstimatorUtil.unnormalizeCovarianceMatrix(correctedEstimate.getCovariance(), scale);
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalStateTransitionMatrix() {
// Un-normalize the state transition matrix (φ) from Hipparchus and return it.
// φ is an mxm matrix where m = nbOrb + nbPropag + nbMeas
// For each element [i,j] of normalized φ (φn), the corresponding physical value is:
// φ[i,j] = φn[i,j] * scale[i] / scale[j]
return correctedEstimate.getStateTransitionMatrix() == null ?
null : KalmanEstimatorUtil.unnormalizeStateTransitionMatrix(correctedEstimate.getStateTransitionMatrix(), scale);
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalMeasurementJacobian() {
// Un-normalize the measurement matrix (H) from Hipparchus and return it.
// H is an nxm matrix where:
// - m = nbOrb + nbPropag + nbMeas is the number of estimated parameters
// - n is the size of the measurement being processed by the filter
// For each element [i,j] of normalized H (Hn) the corresponding physical value is:
// H[i,j] = Hn[i,j] * σ[i] / scale[j]
return correctedEstimate.getMeasurementJacobian() == null ?
null : KalmanEstimatorUtil.unnormalizeMeasurementJacobian(correctedEstimate.getMeasurementJacobian(),
scale,
correctedMeasurement.getObservedMeasurement().getTheoreticalStandardDeviation());
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalInnovationCovarianceMatrix() {
// Un-normalize the innovation covariance matrix (S) from Hipparchus and return it.
// S is an nxn matrix where n is the size of the measurement being processed by the filter
// For each element [i,j] of normalized S (Sn) the corresponding physical value is:
// S[i,j] = Sn[i,j] * σ[i] * σ[j]
return correctedEstimate.getInnovationCovariance() == null ?
null : KalmanEstimatorUtil.unnormalizeInnovationCovarianceMatrix(correctedEstimate.getInnovationCovariance(),
predictedMeasurement.getObservedMeasurement().getTheoreticalStandardDeviation());
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalKalmanGain() {
// Un-normalize the Kalman gain (K) from Hipparchus and return it.
// K is an mxn matrix where:
// - m = nbOrb + nbPropag + nbMeas is the number of estimated parameters
// - n is the size of the measurement being processed by the filter
// For each element [i,j] of normalized K (Kn) the corresponding physical value is:
// K[i,j] = Kn[i,j] * scale[i] / σ[j]
return correctedEstimate.getKalmanGain() == null ?
null : KalmanEstimatorUtil.unnormalizeKalmanGainMatrix(correctedEstimate.getKalmanGain(),
scale,
correctedMeasurement.getObservedMeasurement().getTheoreticalStandardDeviation());
}
/** {@inheritDoc} */
@Override
public int getCurrentMeasurementNumber() {
return currentMeasurementNumber;
}
/** {@inheritDoc} */
@Override
public AbsoluteDate getCurrentDate() {
return currentDate;
}
/** {@inheritDoc} */
@Override
public EstimatedMeasurement<?> getPredictedMeasurement() {
return predictedMeasurement;
}
/** {@inheritDoc} */
@Override
public EstimatedMeasurement<?> getCorrectedMeasurement() {
return correctedMeasurement;
}
/** {@inheritDoc} */
@Override
public void updateNominalSpacecraftState(final SpacecraftState nominal) {
this.nominalMeanSpacecraftState = nominal;
// Update the builder with the nominal mean elements orbit
builder.resetOrbit(nominal.getOrbit(), PropagationType.MEAN);
}
/** Update the reference trajectories using the propagator as input.
* @param propagator The new propagator to use
*/
public void updateReferenceTrajectory(final DSSTPropagator propagator) {
dsstPropagator = propagator;
// Equation name
final String equationName = SemiAnalyticalKalmanEstimator.class.getName() + "-derivatives-";
// Mean state
final SpacecraftState meanState = dsstPropagator.initialIsOsculating() ?
DSSTPropagator.computeMeanState(dsstPropagator.getInitialState(), dsstPropagator.getAttitudeProvider(), dsstPropagator.getAllForceModels()) :
dsstPropagator.getInitialState();
// Update the jacobian harvester
dsstPropagator.setInitialState(meanState, PropagationType.MEAN);
harvester = (DSSTHarvester) dsstPropagator.setupMatricesComputation(equationName, null, null);
}
/** {@inheritDoc} */
@Override
public void updateShortPeriods(final SpacecraftState state) {
// Loop on DSST force models
for (final DSSTForceModel model : builder.getAllForceModels()) {
model.updateShortPeriodTerms(model.getParametersAllValues(), state);
}
harvester.updateFieldShortPeriodTerms(state);
}
/** {@inheritDoc} */
@Override
public void initializeShortPeriodicTerms(final SpacecraftState meanState) {
final List<ShortPeriodTerms> shortPeriodTerms = new ArrayList<ShortPeriodTerms>();
// initialize ForceModels in OSCULATING mode even if propagation is MEAN
final PropagationType type = PropagationType.OSCULATING;
for (final DSSTForceModel force : builder.getAllForceModels()) {
shortPeriodTerms.addAll(force.initializeShortPeriodTerms(new AuxiliaryElements(meanState.getOrbit(), 1), type, force.getParameters(meanState.getDate())));
}
dsstPropagator.setShortPeriodTerms(shortPeriodTerms);
// also need to initialize the Field terms in the same mode
harvester.initializeFieldShortPeriodTerms(meanState, type);
}
/** Get the normalized state transition matrix (STM) from previous point to current point.
* The STM contains the partial derivatives of current state with respect to previous state.
* The STM is an mxm matrix where m is the size of the state vector.
* m = nbOrb + nbPropag + nbMeas
* @return the normalized error state transition matrix
*/
private RealMatrix getErrorStateTransitionMatrix() {
/* The state transition matrix is obtained as follows, with:
* - Phi(k, k-1) : Transitional orbital matrix
* - Psi(k, k-1) : Transitional propagation parameters matrix
*
* | | | . |
* | Phi(k, k-1) | Psi(k, k-1) | ..0.. |
* | | | . |
* |-------------|-------------|--------|
* | . | 1 0 0 | . |
* STM = | ..0.. | 0 1 0 | ..0.. |
* | . | 0 0 1 | . |
* |-------------|-------------|--------|
* | . | . | 1 0 0..|
* | ..0.. | ..0.. | 0 1 0..|
* | . | . | 0 0 1..|
*/
// Initialize to the proper size identity matrix
final RealMatrix stm = MatrixUtils.createRealIdentityMatrix(correctedEstimate.getState().getDimension());
// Derivatives of the state vector with respect to initial state vector
final int nbOrb = getNumberSelectedOrbitalDriversValuesToEstimate();
final RealMatrix dYdY0 = harvester.getB2(nominalMeanSpacecraftState);
// Calculate transitional orbital matrix (See Ref [1], Eq. 3.4a)
final RealMatrix phi = dYdY0.multiply(phiS);
// Fill the state transition matrix with the orbital drivers
final List<DelegatingDriver> drivers = builder.getOrbitalParametersDrivers().getDrivers();
for (int i = 0; i < nbOrb; ++i) {
if (drivers.get(i).isSelected()) {
int jOrb = 0;
for (int j = 0; j < nbOrb; ++j) {
if (drivers.get(j).isSelected()) {
stm.setEntry(i, jOrb++, phi.getEntry(i, j));
}
}
}
}
// Update PhiS
phiS = new QRDecomposition(dYdY0).getSolver().getInverse();
// Derivatives of the state vector with respect to propagation parameters
if (psiS != null) {
final int nbProp = getNumberSelectedPropagationDriversValuesToEstimate();
final RealMatrix dYdPp = harvester.getB3(nominalMeanSpacecraftState);
// Calculate transitional parameters matrix (See Ref [1], Eq. 3.4b)
final RealMatrix psi = dYdPp.subtract(phi.multiply(psiS));
// Fill 1st row, 2nd column (dY/dPp)
for (int i = 0; i < nbOrb; ++i) {
for (int j = 0; j < nbProp; ++j) {
stm.setEntry(i, j + nbOrb, psi.getEntry(i, j));
}
}
// Update PsiS
psiS = dYdPp;
}
// Normalization of the STM
// normalized(STM)ij = STMij*Sj/Si
for (int i = 0; i < scale.length; i++) {
for (int j = 0; j < scale.length; j++ ) {
stm.setEntry(i, j, stm.getEntry(i, j) * scale[j] / scale[i]);
}
}
// Return the error state transition matrix
return stm;
}
/** Get the normalized measurement matrix H.
* H contains the partial derivatives of the measurement with respect to the state.
* H is an nxm matrix where n is the size of the measurement vector and m the size of the state vector.
* @return the normalized measurement matrix H
*/
private RealMatrix getMeasurementMatrix() {
// Observed measurement characteristics
final SpacecraftState evaluationState = predictedMeasurement.getStates()[0];
final ObservedMeasurement<?> observedMeasurement = predictedMeasurement.getObservedMeasurement();
final double[] sigma = predictedMeasurement.getObservedMeasurement().getTheoreticalStandardDeviation();
// Initialize measurement matrix H: nxm
// n: Number of measurements in current measurement
// m: State vector size
final RealMatrix measurementMatrix = MatrixUtils.
createRealMatrix(observedMeasurement.getDimension(),
correctedEstimate.getState().getDimension());
// Predicted orbit
final Orbit predictedOrbit = evaluationState.getOrbit();
// Measurement matrix's columns related to orbital and propagation parameters
// ----------------------------------------------------------
// Partial derivatives of the current Cartesian coordinates with respect to current orbital state
final int nbOrb = getNumberSelectedOrbitalDrivers();
final int nbProp = getNumberSelectedPropagationDrivers();
final double[][] aCY = new double[nbOrb][nbOrb];
predictedOrbit.getJacobianWrtParameters(builder.getPositionAngleType(), aCY);
final RealMatrix dCdY = new Array2DRowRealMatrix(aCY, false);
// Jacobian of the measurement with respect to current Cartesian coordinates
final RealMatrix dMdC = new Array2DRowRealMatrix(predictedMeasurement.getStateDerivatives(0), false);
// Jacobian of the measurement with respect to current orbital state
RealMatrix dMdY = dMdC.multiply(dCdY);
// Compute factor dShortPeriod_dMeanState = I+B1 | B4
final RealMatrix IpB1B4 = MatrixUtils.createRealMatrix(nbOrb, nbOrb + nbProp);
// B1
final RealMatrix B1 = harvester.getB1();
// I + B1
final RealMatrix I = MatrixUtils.createRealIdentityMatrix(nbOrb);
final RealMatrix IpB1 = I.add(B1);
IpB1B4.setSubMatrix(IpB1.getData(), 0, 0);
// If there are not propagation parameters, B4 is null
if (psiS != null) {
final RealMatrix B4 = harvester.getB4();
IpB1B4.setSubMatrix(B4.getData(), 0, nbOrb);
}
// Ref [1], Eq. 3.10
dMdY = dMdY.multiply(IpB1B4);
for (int i = 0; i < dMdY.getRowDimension(); i++) {
for (int j = 0; j < nbOrb; j++) {
final double driverScale = builder.getOrbitalParametersDrivers().getDrivers().get(j).getScale();
measurementMatrix.setEntry(i, j, dMdY.getEntry(i, j) / sigma[i] * driverScale);
}
int col = 0;
for (int j = 0; j < nbProp; j++) {
final double driverScale = estimatedPropagationParameters.getDrivers().get(j).getScale();
for (Span<Double> span = estimatedPropagationParameters.getDrivers().get(j).getValueSpanMap().getFirstSpan();
span != null; span = span.next()) {
measurementMatrix.setEntry(i, col + nbOrb,
dMdY.getEntry(i, col + nbOrb) / sigma[i] * driverScale);
col++;
}
}
}
// Normalized measurement matrix's columns related to measurement parameters
// --------------------------------------------------------------
// Jacobian of the measurement with respect to measurement parameters
// Gather the measurement parameters linked to current measurement
for (final ParameterDriver driver : observedMeasurement.getParametersDrivers()) {
if (driver.isSelected()) {
for (Span<String> span = driver.getNamesSpanMap().getFirstSpan(); span != null; span = span.next()) {
// Derivatives of current measurement w/r to selected measurement parameter
final double[] aMPm = predictedMeasurement.getParameterDerivatives(driver, span.getStart());
// Check that the measurement parameter is managed by the filter
if (measurementParameterColumns.get(span.getData()) != null) {
// Column of the driver in the measurement matrix
final int driverColumn = measurementParameterColumns.get(span.getData());
// Fill the corresponding indexes of the measurement matrix
for (int i = 0; i < aMPm.length; ++i) {
measurementMatrix.setEntry(i, driverColumn, aMPm[i] / sigma[i] * driver.getScale());
}
}
}
}
}
return measurementMatrix;
}
/** Predict the filter correction for the new observation.
* @param stm normalized state transition matrix
* @return the predicted filter correction for the new observation
*/
private RealVector predictFilterCorrection(final RealMatrix stm) {
// Ref [1], Eq. 3.5a and 3.5b
return stm.operate(correctedFilterCorrection);
}
/** Compute the predicted osculating elements.
* @param filterCorrection kalman filter correction
* @return the predicted osculating element
*/
private double[] computeOsculatingElements(final RealVector filterCorrection) {
// Number of estimated orbital parameters
final int nbOrb = getNumberSelectedOrbitalDrivers();
// B1
final RealMatrix B1 = harvester.getB1();
// Short periodic terms
final double[] shortPeriodTerms = dsstPropagator.getShortPeriodTermsValue(nominalMeanSpacecraftState);
// Physical filter correction
final RealVector physicalFilterCorrection = MatrixUtils.createRealVector(nbOrb);
for (int index = 0; index < nbOrb; index++) {
physicalFilterCorrection.addToEntry(index, filterCorrection.getEntry(index) * scale[index]);
}
// B1 * physicalCorrection
final RealVector B1Correction = B1.operate(physicalFilterCorrection);
// Nominal mean elements
final double[] nominalMeanElements = new double[nbOrb];
OrbitType.EQUINOCTIAL.mapOrbitToArray(nominalMeanSpacecraftState.getOrbit(), builder.getPositionAngleType(), nominalMeanElements, null);
// Ref [1] Eq. 3.6
final double[] osculatingElements = new double[nbOrb];
for (int i = 0; i < nbOrb; i++) {
osculatingElements[i] = nominalMeanElements[i] +
physicalFilterCorrection.getEntry(i) +
shortPeriodTerms[i] +
B1Correction.getEntry(i);
}
// Return
return osculatingElements;
}
/** Analytical computation of derivatives.
* This method allow to compute analytical derivatives.
* @param state mean state used to calculate short period perturbations
*/
private void analyticalDerivativeComputations(final SpacecraftState state) {
harvester.setReferenceState(state);
}
/** Get the number of estimated orbital parameters.
* @return the number of estimated orbital parameters
*/
private int getNumberSelectedOrbitalDrivers() {
return estimatedOrbitalParameters.getNbParams();
}
/** Get the number of estimated propagation parameters.
* @return the number of estimated propagation parameters
*/
private int getNumberSelectedPropagationDrivers() {
return estimatedPropagationParameters.getNbParams();
}
/** Get the number of estimated orbital parameters values (some parameter
* driver may have several values to estimate for different time range
* {@link ParameterDriver}.
* @return the number of estimated values for , orbital parameters
*/
private int getNumberSelectedOrbitalDriversValuesToEstimate() {
int nbOrbitalValuesToEstimate = 0;
for (final ParameterDriver driver : estimatedOrbitalParameters.getDrivers()) {
nbOrbitalValuesToEstimate += driver.getNbOfValues();
}
return nbOrbitalValuesToEstimate;
}
/** Get the number of estimated propagation parameters values (some parameter
* driver may have several values to estimate for different time range
* {@link ParameterDriver}.
* @return the number of estimated values for propagation parameters
*/
private int getNumberSelectedPropagationDriversValuesToEstimate() {
int nbPropagationValuesToEstimate = 0;
for (final ParameterDriver driver : estimatedPropagationParameters.getDrivers()) {
nbPropagationValuesToEstimate += driver.getNbOfValues();
}
return nbPropagationValuesToEstimate;
}
/** Get the number of estimated measurement parameters values (some parameter
* driver may have several values to estimate for different time range
* {@link ParameterDriver}.
* @return the number of estimated values for measurement parameters
*/
private int getNumberSelectedMeasurementDriversValuesToEstimate() {
int nbMeasurementValuesToEstimate = 0;
for (final ParameterDriver driver : estimatedMeasurementsParameters.getDrivers()) {
nbMeasurementValuesToEstimate += driver.getNbOfValues();
}
return nbMeasurementValuesToEstimate;
}
/** Update the estimated parameters after the correction phase of the filter.
* The min/max allowed values are handled by the parameter themselves.
*/
private void updateParameters() {
final RealVector correctedState = correctedEstimate.getState();
int i = 0;
// Orbital parameters
for (final DelegatingDriver driver : getEstimatedOrbitalParameters().getDrivers()) {
// let the parameter handle min/max clipping
for (Span<Double> span = driver.getValueSpanMap().getFirstSpan(); span != null; span = span.next()) {
driver.setNormalizedValue(driver.getNormalizedValue(span.getStart()) + correctedState.getEntry(i++), span.getStart());
}
}
// Propagation parameters
for (final DelegatingDriver driver : getEstimatedPropagationParameters().getDrivers()) {
// let the parameter handle min/max clipping
// If the parameter driver contains only 1 value to estimate over the all time range
for (Span<Double> span = driver.getValueSpanMap().getFirstSpan(); span != null; span = span.next()) {
driver.setNormalizedValue(driver.getNormalizedValue(span.getStart()) + correctedState.getEntry(i++), span.getStart());
}
}
// Measurements parameters
for (final DelegatingDriver driver : getEstimatedMeasurementsParameters().getDrivers()) {
// let the parameter handle min/max clipping
for (Span<Double> span = driver.getValueSpanMap().getFirstSpan(); span != null; span = span.next()) {
driver.setNormalizedValue(driver.getNormalizedValue(span.getStart()) + correctedState.getEntry(i++), span.getStart());
}
}
}
}