UnscentedKalmanModel.java
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*
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package org.orekit.estimation.sequential;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.hipparchus.filtering.kalman.ProcessEstimate;
import org.hipparchus.filtering.kalman.unscented.UnscentedEvolution;
import org.hipparchus.filtering.kalman.unscented.UnscentedProcess;
import org.hipparchus.linear.ArrayRealVector;
import org.hipparchus.linear.MatrixUtils;
import org.hipparchus.linear.RealMatrix;
import org.hipparchus.linear.RealVector;
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.orbits.PositionAngleType;
import org.orekit.propagation.Propagator;
import org.orekit.propagation.PropagatorsParallelizer;
import org.orekit.propagation.SpacecraftState;
import org.orekit.propagation.conversion.PropagatorBuilder;
import org.orekit.time.AbsoluteDate;
import org.orekit.utils.ParameterDriver;
import org.orekit.utils.ParameterDriversList;
import org.orekit.utils.ParameterDriversList.DelegatingDriver;
/** Class defining the process model dynamics to use with a {@link UnscentedKalmanEstimator}.
* @author Gaëtan Pierre
* @author Bryan Cazabonne
* @since 11.3
*/
public class UnscentedKalmanModel implements KalmanEstimation, UnscentedProcess<MeasurementDecorator> {
/** Builders for propagators. */
private final List<PropagatorBuilder> builders;
/** Estimated orbital parameters. */
private final ParameterDriversList allEstimatedOrbitalParameters;
/** Estimated propagation drivers. */
private final ParameterDriversList allEstimatedPropagationParameters;
/** Per-builder estimated orbital parameters. */
private final ParameterDriversList[] estimatedOrbitalParameters;
/** Per-builder estimated propagation parameters. */
private final ParameterDriversList[] estimatedPropagationParameters;
/** Estimated measurements parameters. */
private final ParameterDriversList estimatedMeasurementsParameters;
/** Start columns for each estimated orbit. */
private final int[] orbitsStartColumns;
/** End columns for each estimated orbit. */
private final int[] orbitsEndColumns;
/** Map for propagation parameters columns. */
private final Map<String, Integer> propagationParameterColumns;
/** Map for measurements parameters columns. */
private final Map<String, Integer> measurementParameterColumns;
/** Provider for covariance matrice. */
private final List<CovarianceMatrixProvider> covarianceMatrixProviders;
/** Process noise matrix provider for measurement parameters. */
private final CovarianceMatrixProvider measurementProcessNoiseMatrix;
/** Indirection arrays to extract the noise components for estimated parameters. */
private final int[][] covarianceIndirection;
/** Position angle types used during orbit determination. */
private final PositionAngleType[] angleTypes;
/** Orbit types used during orbit determination. */
private final OrbitType[] orbitTypes;
/** Current number of measurement. */
private int currentMeasurementNumber;
/** Current corrected estimate. */
private ProcessEstimate correctedEstimate;
/** Current date. */
private AbsoluteDate currentDate;
/** Previous date. */
private AbsoluteDate previousDate;
/** Predicted spacecraft states. */
private SpacecraftState[] predictedSpacecraftStates;
/** Corrected spacecraft states. */
private SpacecraftState[] correctedSpacecraftStates;
/** Predicted measurement. */
private EstimatedMeasurement<?> predictedMeasurement;
/** Corrected measurement. */
private EstimatedMeasurement<?> correctedMeasurement;
/** Unscented Kalman process model constructor (package private).
* @param propagatorBuilders propagators builders used to evaluate the orbits.
* @param covarianceMatrixProviders provider for covariance matrix
* @param estimatedMeasurementParameters measurement parameters to estimate
* @param measurementProcessNoiseMatrix provider for measurement process noise matrix
*/
protected UnscentedKalmanModel(final List<PropagatorBuilder> propagatorBuilders,
final List<CovarianceMatrixProvider> covarianceMatrixProviders,
final ParameterDriversList estimatedMeasurementParameters,
final CovarianceMatrixProvider measurementProcessNoiseMatrix) {
this.builders = propagatorBuilders;
this.estimatedMeasurementsParameters = estimatedMeasurementParameters;
this.measurementParameterColumns = new HashMap<>(estimatedMeasurementsParameters.getDrivers().size());
this.currentMeasurementNumber = 0;
this.currentDate = propagatorBuilders.get(0).getInitialOrbitDate();
this.previousDate = currentDate;
this.covarianceMatrixProviders = covarianceMatrixProviders;
this.measurementProcessNoiseMatrix = measurementProcessNoiseMatrix;
final Map<String, Integer> orbitalParameterColumns = new HashMap<>(6 * builders.size());
orbitsStartColumns = new int[builders.size()];
orbitsEndColumns = new int[builders.size()];
int columns = 0;
allEstimatedOrbitalParameters = new ParameterDriversList();
estimatedOrbitalParameters = new ParameterDriversList[builders.size()];
// Set estimated orbital parameters
for (int k = 0; k < builders.size(); ++k) {
estimatedOrbitalParameters[k] = new ParameterDriversList();
orbitsStartColumns[k] = columns;
final String suffix = propagatorBuilders.size() > 1 ? "[" + k + "]" : null;
for (final ParameterDriver driver : builders.get(k).getOrbitalParametersDrivers().getDrivers()) {
if (driver.getReferenceDate() == null) {
driver.setReferenceDate(currentDate);
}
if (suffix != null && !driver.getName().endsWith(suffix)) {
// we add suffix only conditionally because the method may already have been called
// and suffixes may have already been appended
driver.setName(driver.getName() + suffix);
}
if (driver.isSelected()) {
allEstimatedOrbitalParameters.add(driver);
estimatedOrbitalParameters[k].add(driver);
orbitalParameterColumns.put(driver.getName(), columns++);
}
}
orbitsEndColumns[k] = columns;
}
// Set estimated propagation parameters
allEstimatedPropagationParameters = new ParameterDriversList();
estimatedPropagationParameters = new ParameterDriversList[builders.size()];
final List<String> estimatedPropagationParametersNames = new ArrayList<>();
for (int k = 0; k < builders.size(); ++k) {
estimatedPropagationParameters[k] = new ParameterDriversList();
for (final ParameterDriver driver : builders.get(k).getPropagationParametersDrivers().getDrivers()) {
if (driver.getReferenceDate() == null) {
driver.setReferenceDate(currentDate);
}
if (driver.isSelected()) {
allEstimatedPropagationParameters.add(driver);
estimatedPropagationParameters[k].add(driver);
final String driverName = driver.getName();
// Add the driver name if it has not been added yet
if (!estimatedPropagationParametersNames.contains(driverName)) {
estimatedPropagationParametersNames.add(driverName);
}
}
}
}
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++);
}
// Populate the map of measurement drivers' columns and update the total number of columns
for (final ParameterDriver parameter : estimatedMeasurementsParameters.getDrivers()) {
// Verify if the driver reference date has been set
if (parameter.getReferenceDate() == null) {
parameter.setReferenceDate(currentDate);
}
measurementParameterColumns.put(parameter.getName(), columns);
columns++;
}
// set angle and orbit types
angleTypes = new PositionAngleType[builders.size()];
orbitTypes = new OrbitType[builders.size()];
for (int k = 0; k < builders.size(); k++) {
angleTypes[k] = builders.get(k).getPositionAngleType();
orbitTypes[k] = builders.get(k).getOrbitType();
}
// set covariance indirection
this.covarianceIndirection = new int[covarianceMatrixProviders.size()][columns];
for (int k = 0; k < covarianceIndirection.length; ++k) {
final ParameterDriversList orbitDrivers = builders.get(k).getOrbitalParametersDrivers();
final ParameterDriversList parametersDrivers = builders.get(k).getPropagationParametersDrivers();
Arrays.fill(covarianceIndirection[k], -1);
int i = 0;
for (final ParameterDriver driver : orbitDrivers.getDrivers()) {
final Integer c = orbitalParameterColumns.get(driver.getName());
covarianceIndirection[k][i++] = (c == null) ? -1 : c.intValue();
}
for (final ParameterDriver driver : parametersDrivers.getDrivers()) {
final Integer c = propagationParameterColumns.get(driver.getName());
if (c != null) {
covarianceIndirection[k][i++] = c.intValue();
}
}
for (final ParameterDriver driver : estimatedMeasurementParameters.getDrivers()) {
final Integer c = measurementParameterColumns.get(driver.getName());
if (c != null) {
covarianceIndirection[k][i++] = c.intValue();
}
}
}
// Initialize the estimated state and fill its values
final RealVector correctedState = MatrixUtils.createRealVector(columns);
int p = 0;
for (final ParameterDriver driver : allEstimatedOrbitalParameters.getDrivers()) {
correctedState.setEntry(p++, driver.getValue());
}
for (final ParameterDriver driver : allEstimatedPropagationParameters.getDrivers()) {
correctedState.setEntry(p++, driver.getValue());
}
for (final ParameterDriver driver : estimatedMeasurementsParameters.getDrivers()) {
correctedState.setEntry(p++, driver.getValue());
}
this.predictedSpacecraftStates = new SpacecraftState[propagatorBuilders.size()];
for (int i = 0; i < propagatorBuilders.size(); i++) {
predictedSpacecraftStates[i] = propagatorBuilders.get(i).buildPropagator(propagatorBuilders.get(i).getSelectedNormalizedParameters()).getInitialState();
}
this.correctedSpacecraftStates = predictedSpacecraftStates.clone();
// Number of estimated measurement parameters
final int nbMeas = estimatedMeasurementParameters.getNbParams();
final RealMatrix correctedCovariance = MatrixUtils.createRealMatrix(columns, columns);
for (int k = 0; k < covarianceMatrixProviders.size(); k++) {
// Number of estimated dynamic parameters (orbital + propagation)
final int nbDyn = orbitsEndColumns[k] - orbitsStartColumns[k] +
estimatedPropagationParameters[k].getNbParams();
// Covariance matrix
final RealMatrix noiseK = MatrixUtils.createRealMatrix(nbDyn + nbMeas, nbDyn + nbMeas);
final RealMatrix noiseP = covarianceMatrixProviders.get(k).
getInitialCovarianceMatrix(correctedSpacecraftStates[k]);
noiseK.setSubMatrix(noiseP.getData(), 0, 0);
if (measurementProcessNoiseMatrix != null) {
final RealMatrix noiseM = measurementProcessNoiseMatrix.
getInitialCovarianceMatrix(correctedSpacecraftStates[k]);
noiseK.setSubMatrix(noiseM.getData(), nbDyn, nbDyn);
}
KalmanEstimatorUtil.checkDimension(noiseK.getRowDimension(),
builders.get(k).getOrbitalParametersDrivers(),
builders.get(k).getPropagationParametersDrivers(),
estimatedMeasurementsParameters);
final int[] indK = covarianceIndirection[k];
for (int i = 0; i < indK.length; ++i) {
if (indK[i] >= 0) {
for (int j = 0; j < indK.length; ++j) {
if (indK[j] >= 0) {
correctedCovariance.setEntry(indK[i], indK[j], noiseK.getEntry(i, j));
}
}
}
}
}
// Initialize corrected estimate
this.correctedEstimate = new ProcessEstimate(0.0, correctedState, correctedCovariance);
}
/** {@inheritDoc} */
@Override
public UnscentedEvolution getEvolution(final double previousTime, final RealVector[] sigmaPoints,
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(builders.get(0).getInitialOrbitDate());
}
}
// Increment measurement number
++currentMeasurementNumber;
// Update the current date
currentDate = measurement.getObservedMeasurement().getDate();
// Initialize arrays of predicted states and measurements
final RealVector[] predictedStates = new RealVector[sigmaPoints.length];
// Loop on builders
for (int k = 0; k < builders.size(); k++ ) {
// Sigma points for the current builder
final RealVector[] currentSigmaPoints = new ArrayRealVector[sigmaPoints.length];
for (int i = 0; i < sigmaPoints.length; i++) {
currentSigmaPoints[i] = sigmaPoints[i].getSubVector(orbitsStartColumns[k], orbitsEndColumns[k] - orbitsStartColumns[k]);
}
// Predict states
final List<SpacecraftState> states = predictStates(currentSigmaPoints, k);
// Loop on states
for (int i = 0; i < states.size(); ++i) {
if (k == 0) {
predictedStates[i] = new ArrayRealVector(sigmaPoints[i].getDimension());
}
// Current predicted state
final SpacecraftState predicted = states.get(i);
// First, convert the predicted state to an array
final double[] predictedArray = new double[currentSigmaPoints[i].getDimension()];
orbitTypes[k].mapOrbitToArray(predicted.getOrbit(), angleTypes[k], predictedArray, null);
predictedStates[i].setSubVector(orbitsStartColumns[k], new ArrayRealVector(predictedArray));
}
}
// compute process noise matrix
final RealMatrix processNoise = MatrixUtils.createRealMatrix(sigmaPoints[0].getDimension(), sigmaPoints[0].getDimension());
for (int k = 0; k < covarianceMatrixProviders.size(); ++k) {
// Number of estimated measurement parameters
final int nbMeas = estimatedMeasurementsParameters.getNbParams();
// Number of estimated dynamic parameters (orbital + propagation)
final int nbDyn = orbitsEndColumns[k] - orbitsStartColumns[k] +
estimatedPropagationParameters[k].getNbParams();
// Covariance matrix
final RealMatrix noiseK = MatrixUtils.createRealMatrix(nbDyn + nbMeas, nbDyn + nbMeas);
final RealMatrix noiseP = covarianceMatrixProviders.get(k).
getProcessNoiseMatrix(correctedSpacecraftStates[k],
predictedSpacecraftStates[k]);
noiseK.setSubMatrix(noiseP.getData(), 0, 0);
if (measurementProcessNoiseMatrix != null) {
final RealMatrix noiseM = measurementProcessNoiseMatrix.
getProcessNoiseMatrix(correctedSpacecraftStates[k],
predictedSpacecraftStates[k]);
noiseK.setSubMatrix(noiseM.getData(), nbDyn, nbDyn);
}
KalmanEstimatorUtil.checkDimension(noiseK.getRowDimension(),
builders.get(k).getOrbitalParametersDrivers(),
builders.get(k).getPropagationParametersDrivers(),
estimatedMeasurementsParameters);
final int[] indK = covarianceIndirection[k];
for (int i = 0; i < indK.length; ++i) {
if (indK[i] >= 0) {
for (int j = 0; j < indK.length; ++j) {
if (indK[j] >= 0) {
processNoise.setEntry(indK[i], indK[j], noiseK.getEntry(i, j));
}
}
}
}
}
// Update epoch
previousDate = currentDate;
// Return
return new UnscentedEvolution(measurement.getTime(), predictedStates, processNoise);
}
/** {@inheritDoc} */
@Override
public RealVector[] getPredictedMeasurements(final RealVector[] predictedSigmaPoints, final MeasurementDecorator measurement) {
// Observed measurement
final ObservedMeasurement<?> observedMeasurement = measurement.getObservedMeasurement();
// Initialize arrays of predicted states and measurements
final RealVector[] predictedMeasurements = new RealVector[predictedSigmaPoints.length];
// Initialize the relevant states used for measurement estimation
final SpacecraftState[][] statesForMeasurementEstimation = new SpacecraftState[predictedSigmaPoints.length][builders.size()];
// Convert sigma points to spacecraft states
for (int k = 0; k < builders.size(); k++ ) {
// Loop on sigma points
for (int i = 0; i < predictedSigmaPoints.length; i++) {
final SpacecraftState state = new SpacecraftState(orbitTypes[k].mapArrayToOrbit(predictedSigmaPoints[i].toArray(), null,
angleTypes[k], currentDate, builders.get(k).getMu(),
builders.get(k).getFrame()));
statesForMeasurementEstimation[i][k] = state;
}
}
// Loop on sigma points to predict measurements
for (int i = 0; i < predictedSigmaPoints.length; i++) {
final EstimatedMeasurement<?> estimated = observedMeasurement.estimate(currentMeasurementNumber, currentMeasurementNumber,
KalmanEstimatorUtil.filterRelevant(observedMeasurement,
statesForMeasurementEstimation[i]));
predictedMeasurements[i] = new ArrayRealVector(estimated.getEstimatedValue());
}
// Return the predicted measurements
return predictedMeasurements;
}
/** {@inheritDoc} */
@Override
public RealVector getInnovation(final MeasurementDecorator measurement, final RealVector predictedMeas,
final RealVector predictedState, final RealMatrix innovationCovarianceMatrix) {
// Loop on builders
for (int k = 0; k < builders.size(); k++) {
// Update predicted states
final double[] predictedStateArray = predictedState.getSubVector(orbitsStartColumns[k], orbitsEndColumns[k] - orbitsStartColumns[k]).toArray();
final Orbit predictedOrbit = orbitTypes[k].mapArrayToOrbit(predictedStateArray, null, angleTypes[k],
currentDate, builders.get(k).getMu(), builders.get(k).getFrame());
predictedSpacecraftStates[k] = new SpacecraftState(predictedOrbit);
// Update the builder with the predicted orbit
builders.get(k).resetOrbit(predictedOrbit);
}
// Predicted measurement
predictedMeasurement = measurement.getObservedMeasurement().estimate(currentMeasurementNumber, currentMeasurementNumber,
KalmanEstimatorUtil.filterRelevant(measurement.getObservedMeasurement(),
predictedSpacecraftStates));
predictedMeasurement.setEstimatedValue(predictedMeas.toArray());
// set estimated value to the predicted value by the filter
KalmanEstimatorUtil.applyDynamicOutlierFilter(predictedMeasurement, innovationCovarianceMatrix);
// Compute the innovation vector (not normalized for unscented Kalman filter)
return KalmanEstimatorUtil.computeInnovationVector(predictedMeasurement);
}
/**
* Predict the predicted states for the given sigma points.
* @param sigmaPoints current sigma points
* @param index the index corresponding to the satellite one is dealing with
* @return predicted state for the given sigma point
*/
private List<SpacecraftState> predictStates(final RealVector[] sigmaPoints, final int index) {
// Loop on sigma points to create the propagator parallelizer
final List<Propagator> propagators = new ArrayList<>(sigmaPoints.length);
for (int k = 0; k < sigmaPoints.length; ++k) {
// Current sigma point
final double[] currentPoint = sigmaPoints[k].copy().toArray();
// Create the corresponding orbit propagator
final Propagator currentPropagator = createPropagator(currentPoint, index);
// Add it to the list of propagators
propagators.add(currentPropagator);
}
// Create the propagator parallelizer and predict states
// (the shift is done to start a little bit before the previous measurement epoch)
final PropagatorsParallelizer parallelizer = new PropagatorsParallelizer(propagators, interpolators -> { });
final List<SpacecraftState> states = parallelizer.propagate(previousDate.shiftedBy(-1.0e-3), currentDate);
// Return
return states;
}
/**
* Create a propagator for the given sigma point.
* @param point input sigma point
* @param index the index corresponding to the satellite one is dealing with
* @return the corresponding orbit propagator
*/
private Propagator createPropagator(final double[] point, final int index) {
// Create a new instance of the current propagator builder
final PropagatorBuilder copy = builders.get(index).copy();
// Convert the given sigma point to an orbit
final Orbit orbit = orbitTypes[index].mapArrayToOrbit(point, null, angleTypes[index], copy.getInitialOrbitDate(),
copy.getMu(), copy.getFrame());
copy.resetOrbit(orbit);
// Create the propagator
final Propagator propagator = copy.buildPropagator(copy.getSelectedNormalizedParameters());
return propagator;
}
/** Finalize estimation.
* @param observedMeasurement measurement that has just been processed
* @param estimate corrected estimate
*/
public void finalizeEstimation(final ObservedMeasurement<?> observedMeasurement,
final ProcessEstimate estimate) {
correctedEstimate = estimate;
// Loop on builders
for (int k = 0; k < builders.size(); k++ ) {
// Update corrected state
final double[] correctedStateArray = estimate.getState().getSubVector(orbitsStartColumns[k], orbitsEndColumns[k] - orbitsStartColumns[k]).toArray();
final Orbit correctedOrbit = orbitTypes[k].mapArrayToOrbit(correctedStateArray, null, angleTypes[k],
currentDate, builders.get(k).getMu(), builders.get(k).getFrame());
correctedSpacecraftStates[k] = new SpacecraftState(correctedOrbit);
// Update the builder
builders.get(k).resetOrbit(correctedOrbit);
}
// Corrected measurement
correctedMeasurement = observedMeasurement.estimate(currentMeasurementNumber,
currentMeasurementNumber,
KalmanEstimatorUtil.filterRelevant(observedMeasurement,
getCorrectedSpacecraftStates()));
}
/** Get the propagators estimated with the values set in the propagators builders.
* @return propagators based on the current values in the builder
*/
public Propagator[] getEstimatedPropagators() {
// Return propagators built with current instantiation of the propagator builders
final Propagator[] propagators = new Propagator[builders.size()];
for (int k = 0; k < builders.size(); ++k) {
propagators[k] = builders.get(k).buildPropagator(builders.get(k).getSelectedNormalizedParameters());
}
return propagators;
}
/** Get the current corrected estimate.
* @return current corrected estimate
*/
public ProcessEstimate getEstimate() {
return correctedEstimate;
}
/** {@inheritDoc} */
@Override
public ParameterDriversList getEstimatedOrbitalParameters() {
return allEstimatedOrbitalParameters;
}
/** {@inheritDoc} */
@Override
public ParameterDriversList getEstimatedPropagationParameters() {
return allEstimatedPropagationParameters;
}
/** {@inheritDoc} */
@Override
public ParameterDriversList getEstimatedMeasurementsParameters() {
return estimatedMeasurementsParameters;
}
/** {@inheritDoc} */
@Override
public SpacecraftState[] getPredictedSpacecraftStates() {
return predictedSpacecraftStates.clone();
}
/** {@inheritDoc} */
@Override
public SpacecraftState[] getCorrectedSpacecraftStates() {
return correctedSpacecraftStates.clone();
}
/** {@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(getEstimate().getState().getDimension());
int i = 0;
for (final DelegatingDriver driver : getEstimatedOrbitalParameters().getDrivers()) {
physicalEstimatedState.setEntry(i++, driver.getValue());
}
for (final DelegatingDriver driver : getEstimatedPropagationParameters().getDrivers()) {
physicalEstimatedState.setEntry(i++, driver.getValue());
}
for (final DelegatingDriver driver : getEstimatedMeasurementsParameters().getDrivers()) {
physicalEstimatedState.setEntry(i++, driver.getValue());
}
return physicalEstimatedState;
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalEstimatedCovarianceMatrix() {
return correctedEstimate.getCovariance();
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalStateTransitionMatrix() {
return null;
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalMeasurementJacobian() {
return null;
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalInnovationCovarianceMatrix() {
return correctedEstimate.getInnovationCovariance();
}
/** {@inheritDoc} */
@Override
public RealMatrix getPhysicalKalmanGain() {
return correctedEstimate.getKalmanGain();
}
/** {@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;
}
}