UnivariateProcessNoise.java
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package org.orekit.estimation.sequential;
import org.hipparchus.analysis.UnivariateFunction;
import org.hipparchus.linear.MatrixUtils;
import org.hipparchus.linear.RealMatrix;
import org.orekit.frames.LOFType;
import org.orekit.orbits.PositionAngle;
import org.orekit.propagation.SpacecraftState;
import org.orekit.utils.CartesianDerivativesFilter;
/** Provider for a temporal evolution of the process noise matrix.
* All parameters (orbital or propagation) are time dependent and provided as {@link UnivariateFunction}.
* The argument of the functions is a duration in seconds (between current and previous spacecraft state).
* The output of the functions must be of the dimension of a standard deviation.
* The method {@link #getProcessNoiseMatrix} then square the values so that they are consistent with a covariance matrix.
* <p>
* The orbital parameters evolutions are provided in LOF frame and Cartesian (PV);
* then converted in inertial frame and current {@link org.orekit.orbits.OrbitType} and {@link PositionAngle}
* when method {@link #getProcessNoiseMatrix} is called.
* </p>
* <p>
* The time-dependent functions define a process noise matrix that is diagonal
* <em>in the Local Orbital Frame</em>, corresponds to Cartesian elements, abd represents
* the temporal evolution of (the standard deviation of) the process noise model. The
* first function is therefore the standard deviation along the LOF X axis, the second
* function represents the standard deviation along the LOF Y axis...
* This allows to set up simply a process noise representing an uncertainty that grows
* mainly along the track. The 6x6 upper left part of output matrix will however not be
* diagonal as it will be converted to the same inertial frame and orbit type as the
* {@link SpacecraftState state} used by the {@link KalmanEstimator Kalman estimator}.
* </p>
* <p>
* The propagation and measurements parameters are not associated to a specific frame and
* are appended as is in the lower right part diagonal of the output matrix. This implies
* this simplified model does not include correlation between the parameters and the orbit,
* but only evolution of the parameters themselves. If such correlations are needed, users
* must set up a custom {@link CovarianceMatrixProvider covariance matrix provider}. In most
* cases, the parameters are constant and their evolution noise is always 0, so the
* functions can be set to {@code x -> 0}.
* </p>
* <p>
* This class always provides one initial noise matrix or initial covariance matrix and one process noise matrix.
* </p>
* @author Luc Maisonobe
* @author Maxime Journot
* @since 9.2
*/
public class UnivariateProcessNoise extends AbstractCovarianceMatrixProvider {
/** Local Orbital Frame (LOF) type used. */
private final LOFType lofType;
/** Position angle for the orbital process noise matrix. */
private final PositionAngle positionAngle;
/** Array of univariate functions for the six orbital parameters process noise evolution in LOF frame and Cartesian formalism. */
private final UnivariateFunction[] lofCartesianOrbitalParametersEvolution;
/** Array of univariate functions for the propagation parameters process noise evolution. */
private final UnivariateFunction[] propagationParametersEvolution;
/** Array of univariate functions for the measurements parameters process noise evolution. */
private final UnivariateFunction[] measurementsParametersEvolution;
/** Simple constructor.
* @param initialCovarianceMatrix initial covariance matrix
* @param lofType the LOF type used
* @param positionAngle the position angle used for the computation of the process noise
* @param lofCartesianOrbitalParametersEvolution Array of univariate functions for the six orbital parameters process noise evolution in LOF frame and Cartesian orbit type
* @param propagationParametersEvolution Array of univariate functions for the propagation parameters process noise evolution
*/
public UnivariateProcessNoise(final RealMatrix initialCovarianceMatrix,
final LOFType lofType,
final PositionAngle positionAngle,
final UnivariateFunction[] lofCartesianOrbitalParametersEvolution,
final UnivariateFunction[] propagationParametersEvolution) {
// Call the new constructor with an empty array for measurements parameters
this(initialCovarianceMatrix, lofType, positionAngle, lofCartesianOrbitalParametersEvolution, propagationParametersEvolution, new UnivariateFunction[0]);
}
/** Simple constructor.
* @param initialCovarianceMatrix initial covariance matrix
* @param lofType the LOF type used
* @param positionAngle the position angle used for the computation of the process noise
* @param lofCartesianOrbitalParametersEvolution Array of univariate functions for the six orbital parameters process noise evolution in LOF frame and Cartesian orbit type
* @param propagationParametersEvolution Array of univariate functions for the propagation parameters process noise evolution
* @param measurementsParametersEvolution Array of univariate functions for the measurements parameters process noise evolution
* @since 10.3
*/
public UnivariateProcessNoise(final RealMatrix initialCovarianceMatrix,
final LOFType lofType,
final PositionAngle positionAngle,
final UnivariateFunction[] lofCartesianOrbitalParametersEvolution,
final UnivariateFunction[] propagationParametersEvolution,
final UnivariateFunction[] measurementsParametersEvolution) {
super(initialCovarianceMatrix);
this.lofType = lofType;
this.positionAngle = positionAngle;
this.lofCartesianOrbitalParametersEvolution = lofCartesianOrbitalParametersEvolution.clone();
this.propagationParametersEvolution = propagationParametersEvolution.clone();
this.measurementsParametersEvolution = measurementsParametersEvolution.clone();
}
/** Getter for the lofType.
* @return the lofType
*/
public LOFType getLofType() {
return lofType;
}
/** Getter for the positionAngle.
* @return the positionAngle
*/
public PositionAngle getPositionAngle() {
return positionAngle;
}
/** Getter for the lofCartesianOrbitalParametersEvolution.
* @return the lofCartesianOrbitalParametersEvolution
*/
public UnivariateFunction[] getLofCartesianOrbitalParametersEvolution() {
return lofCartesianOrbitalParametersEvolution.clone();
}
/** Getter for the propagationParametersEvolution.
* @return the propagationParametersEvolution
*/
public UnivariateFunction[] getPropagationParametersEvolution() {
return propagationParametersEvolution.clone();
}
/** Getter for the measurementsParametersEvolution.
* @return the measurementsParametersEvolution
*/
public UnivariateFunction[] getMeasurementsParametersEvolution() {
return measurementsParametersEvolution.clone();
}
/** {@inheritDoc} */
@Override
public RealMatrix getProcessNoiseMatrix(final SpacecraftState previous,
final SpacecraftState current) {
// Number of estimated parameters
final int nbOrb = lofCartesianOrbitalParametersEvolution.length;
final int nbPropag = propagationParametersEvolution.length;
final int nbMeas = measurementsParametersEvolution.length;
// Initialize process noise matrix
final RealMatrix processNoiseMatrix = MatrixUtils.createRealMatrix(nbOrb + nbPropag + nbMeas,
nbOrb + nbPropag + nbMeas);
// Orbital parameters
if (nbOrb != 0) {
// Orbital parameters process noise matrix in inertial frame and current orbit type
final RealMatrix inertialOrbitalProcessNoiseMatrix = getInertialOrbitalProcessNoiseMatrix(previous, current);
processNoiseMatrix.setSubMatrix(inertialOrbitalProcessNoiseMatrix.getData(), 0, 0);
}
// Propagation parameters
if (nbPropag != 0) {
// Propagation parameters process noise matrix
final RealMatrix propagationProcessNoiseMatrix = getPropagationProcessNoiseMatrix(previous, current);
processNoiseMatrix.setSubMatrix(propagationProcessNoiseMatrix.getData(), nbOrb, nbOrb);
}
// Measurement parameters
if (nbMeas != 0) {
// Measurement parameters process noise matrix
final RealMatrix measurementsProcessNoiseMatrix = getMeasurementsProcessNoiseMatrix(previous, current);
processNoiseMatrix.setSubMatrix(measurementsProcessNoiseMatrix.getData(), nbOrb + nbPropag, nbOrb + nbPropag);
}
return processNoiseMatrix;
}
/** Get the process noise for the six orbital parameters in current spacecraft inertial frame and current orbit type.
* @param previous previous state
* @param current current state
* @return physical (i.e. non normalized) orbital process noise matrix in inertial frame
*/
private RealMatrix getInertialOrbitalProcessNoiseMatrix(final SpacecraftState previous,
final SpacecraftState current) {
// ΔT = duration in seconds from previous to current spacecraft state
final double deltaT = current.getDate().durationFrom(previous.getDate());
// Evaluate the functions, using ΔT as argument
final int lofOrbitalprocessNoiseLength = lofCartesianOrbitalParametersEvolution.length;
final double[] lofOrbitalProcessNoiseValues = new double[lofOrbitalprocessNoiseLength];
// The function return a value which dimension is that of a standard deviation
// It needs to be squared before being put in the process noise covariance matrix
for (int i = 0; i < lofOrbitalprocessNoiseLength; i++) {
final double functionValue = lofCartesianOrbitalParametersEvolution[i].value(deltaT);
lofOrbitalProcessNoiseValues[i] = functionValue * functionValue;
}
// Form the diagonal matrix in LOF frame and Cartesian formalism
final RealMatrix lofCartesianProcessNoiseMatrix = MatrixUtils.createRealDiagonalMatrix(lofOrbitalProcessNoiseValues);
// Get the Jacobian from LOF to Inertial
final double[][] dLofdInertial = new double[6][6];
lofType.transformFromInertial(current.getDate(),
current.getPVCoordinates()).getInverse().getJacobian(CartesianDerivativesFilter.USE_PV,
dLofdInertial);
final RealMatrix jacLofToInertial = MatrixUtils.createRealMatrix(dLofdInertial);
// Jacobian of orbit parameters with respect to Cartesian parameters
final double[][] dYdC = new double[6][6];
current.getOrbit().getJacobianWrtCartesian(positionAngle, dYdC);
final RealMatrix jacOrbitWrtCartesian = MatrixUtils.createRealMatrix(dYdC);
// Complete Jacobian of the transformation
final RealMatrix jacobian = jacOrbitWrtCartesian.multiply(jacLofToInertial);
// Return the orbital process noise matrix in inertial frame and proper orbit type
final RealMatrix inertialOrbitalProcessNoiseMatrix = jacobian.
multiply(lofCartesianProcessNoiseMatrix).
multiplyTransposed(jacobian);
return inertialOrbitalProcessNoiseMatrix;
}
/** Get the process noise for the propagation parameters.
* @param previous previous state
* @param current current state
* @return physical (i.e. non normalized) propagation process noise matrix
*/
private RealMatrix getPropagationProcessNoiseMatrix(final SpacecraftState previous,
final SpacecraftState current) {
// ΔT = duration from previous to current spacecraft state (in seconds)
final double deltaT = current.getDate().durationFrom(previous.getDate());
// Evaluate the functions, using ΔT as argument
final int propagationProcessNoiseLength = propagationParametersEvolution.length;
final double[] propagationProcessNoiseValues = new double[propagationProcessNoiseLength];
// The function return a value which dimension is that of a standard deviation
// It needs to be squared before being put in the process noise covariance matrix
for (int i = 0; i < propagationProcessNoiseLength; i++) {
final double functionValue = propagationParametersEvolution[i].value(deltaT);
propagationProcessNoiseValues[i] = functionValue * functionValue;
}
// Form the diagonal matrix corresponding to propagation parameters process noise
return MatrixUtils.createRealDiagonalMatrix(propagationProcessNoiseValues);
}
/** Get the process noise for the measurements parameters.
* @param previous previous state
* @param current current state
* @return physical (i.e. non normalized) measurements process noise matrix
*/
private RealMatrix getMeasurementsProcessNoiseMatrix(final SpacecraftState previous,
final SpacecraftState current) {
// ΔT = duration from previous to current spacecraft state (in seconds)
final double deltaT = current.getDate().durationFrom(previous.getDate());
// Evaluate the functions, using ΔT as argument
final int measurementsProcessNoiseLength = measurementsParametersEvolution.length;
final double[] measurementsProcessNoiseValues = new double[measurementsProcessNoiseLength];
// The function return a value which dimension is that of a standard deviation
// It needs to be squared before being put in the process noise covariance matrix
for (int i = 0; i < measurementsProcessNoiseLength; i++) {
final double functionValue = measurementsParametersEvolution[i].value(deltaT);
measurementsProcessNoiseValues[i] = functionValue * functionValue;
}
// Form the diagonal matrix corresponding to propagation parameters process noise
return MatrixUtils.createRealDiagonalMatrix(measurementsProcessNoiseValues);
}
}