AbstractPropagatorConverter.java
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* this work for additional information regarding copyright ownership.
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package org.orekit.propagation.conversion;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.hipparchus.analysis.MultivariateVectorFunction;
import org.hipparchus.exception.MathRuntimeException;
import org.hipparchus.linear.DiagonalMatrix;
import org.hipparchus.optim.ConvergenceChecker;
import org.hipparchus.optim.SimpleVectorValueChecker;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer;
import org.hipparchus.optim.nonlinear.vector.leastsquares.MultivariateJacobianFunction;
import org.hipparchus.util.FastMath;
import org.orekit.errors.OrekitException;
import org.orekit.errors.OrekitMessages;
import org.orekit.frames.Frame;
import org.orekit.propagation.Propagator;
import org.orekit.propagation.SpacecraftState;
import org.orekit.propagation.integration.AbstractIntegratedPropagator;
import org.orekit.time.AbsoluteDate;
import org.orekit.utils.PVCoordinates;
import org.orekit.utils.ParameterDriver;
/** Common handling of {@link PropagatorConverter} methods for propagators conversions.
* <p>
* This abstract class factors the common code for propagators conversion.
* Only one method must be implemented by derived classes: {@link #getObjectiveFunction()}.
* </p>
* <p>
* The converter uses the LevenbergMarquardtOptimizer from the <a
* href="https://hipparchus.org/">Hipparchus</a> library.
* Different implementations correspond to different methods for computing the Jacobian.
* </p>
* @author Pascal Parraud
* @since 6.0
*/
public abstract class AbstractPropagatorConverter implements PropagatorConverter {
/** Spacecraft states sample. */
private List<SpacecraftState> sample;
/** Target position and velocities at sample points. */
private double[] target;
/** Weight for residuals. */
private double[] weight;
/** Auxiliary outputData: RMS of solution. */
private double rms;
/** Position use indicator. */
private boolean onlyPosition;
/** Adapted propagator. */
private Propagator adapted;
/** Propagator builder. */
private final PropagatorBuilder builder;
/** Frame. */
private final Frame frame;
/** Optimizer for fitting. */
private final LevenbergMarquardtOptimizer optimizer;
/** Optimum found. */
private LeastSquaresOptimizer.Optimum optimum;
/** Convergence checker for optimization algorithm. */
private final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker;
/** Maximum number of iterations for optimization. */
private final int maxIterations;
/** Build a new instance.
* @param builder propagator builder
* @param threshold absolute convergence threshold for optimization algorithm
* @param maxIterations maximum number of iterations for fitting
*/
protected AbstractPropagatorConverter(final PropagatorBuilder builder,
final double threshold,
final int maxIterations) {
this.builder = builder;
this.frame = builder.getFrame();
this.optimizer = new LevenbergMarquardtOptimizer();
this.maxIterations = maxIterations;
this.sample = new ArrayList<SpacecraftState>();
final SimpleVectorValueChecker svvc = new SimpleVectorValueChecker(-1.0, threshold);
this.checker = LeastSquaresFactory.evaluationChecker(svvc);
}
/** Convert a propagator to another.
* @param source initial propagator (the propagator will be used for sample
* generation, if it is a numerical propagator, its initial state will
* be reset unless {@link AbstractIntegratedPropagator#setResetAtEnd(boolean)}
* has been called beforehand)
* @param timeSpan time span for fitting
* @param nbPoints number of fitting points over time span
* @param freeParameters names of the free parameters
* @return adapted propagator
* @exception IllegalArgumentException if one of the parameters cannot be free
*/
public Propagator convert(final Propagator source,
final double timeSpan,
final int nbPoints,
final List<String> freeParameters)
throws IllegalArgumentException {
setFreeParameters(freeParameters);
final List<SpacecraftState> states = createSample(source, timeSpan, nbPoints);
return convert(states, false, freeParameters);
}
/** Convert a propagator to another.
* @param source initial propagator (the propagator will be used for sample
* generation, if it is a numerical propagator, its initial state will
* be reset unless {@link AbstractIntegratedPropagator#setResetAtEnd(boolean)}
* has been called beforehand)
* @param timeSpan time span for fitting
* @param nbPoints number of fitting points over time span
* @param freeParameters names of the free parameters
* @return adapted propagator
* @exception IllegalArgumentException if one of the parameters cannot be free
*/
public Propagator convert(final Propagator source,
final double timeSpan,
final int nbPoints,
final String... freeParameters)
throws IllegalArgumentException {
setFreeParameters(Arrays.asList(freeParameters));
final List<SpacecraftState> states = createSample(source, timeSpan, nbPoints);
return convert(states, false, freeParameters);
}
/** Find the propagator that minimize the mean square error for a sample of {@link SpacecraftState states}.
* @param states spacecraft states sample to fit
* @param positionOnly if true, consider only position data otherwise both position and velocity are used
* @param freeParameters names of the free parameters
* @return adapted propagator
* @exception IllegalArgumentException if one of the parameters cannot be free
*/
public Propagator convert(final List<SpacecraftState> states,
final boolean positionOnly,
final List<String> freeParameters)
throws IllegalArgumentException {
setFreeParameters(freeParameters);
return adapt(states, positionOnly);
}
/** Find the propagator that minimize the mean square error for a sample of {@link SpacecraftState states}.
* @param states spacecraft states sample to fit
* @param positionOnly if true, consider only position data otherwise both position and velocity are used
* @param freeParameters names of the free parameters
* @return adapted propagator
* @exception IllegalArgumentException if one of the parameters cannot be free
*/
public Propagator convert(final List<SpacecraftState> states,
final boolean positionOnly,
final String... freeParameters)
throws IllegalArgumentException {
setFreeParameters(Arrays.asList(freeParameters));
return adapt(states, positionOnly);
}
/** Get the adapted propagator.
* @return adapted propagator
*/
public Propagator getAdaptedPropagator() {
return adapted;
}
/** Get the Root Mean Square Deviation of the fitting.
* @return RMSD
*/
public double getRMS() {
return rms;
}
/** Get the number of objective function evaluations.
* @return the number of objective function evaluations.
*/
public int getEvaluations() {
return optimum.getEvaluations();
}
/** Get the function computing position/velocity at sample points.
* @return function computing position/velocity at sample points
*/
protected abstract MultivariateVectorFunction getObjectiveFunction();
/** Get the Jacobian of the function computing position/velocity at sample points.
* @return Jacobian of the function computing position/velocity at sample points
*/
protected abstract MultivariateJacobianFunction getModel();
/** Check if fitting uses only sample positions.
* @return true if fitting uses only sample positions
*/
protected boolean isOnlyPosition() {
return onlyPosition;
}
/** Get the size of the target.
* @return target size
*/
protected int getTargetSize() {
return target.length;
}
/** Get the frame of the initial state.
* @return the orbit frame
*/
protected Frame getFrame() {
return frame;
}
/** Get the states sample.
* @return the states sample
*/
protected List<SpacecraftState> getSample() {
return sample;
}
/** Create a sample of {@link SpacecraftState}.
* @param source initial propagator
* @param timeSpan time span for the sample
* @param nbPoints number of points for the sample over the time span
* @return a sample of {@link SpacecraftState}
*/
private List<SpacecraftState> createSample(final Propagator source,
final double timeSpan,
final int nbPoints) {
final List<SpacecraftState> states = new ArrayList<SpacecraftState>();
final double stepSize = timeSpan / (nbPoints - 1);
final AbsoluteDate iniDate = source.getInitialState().getDate();
for (double dt = 0; dt < timeSpan; dt += stepSize) {
states.add(source.propagate(iniDate.shiftedBy(dt)));
}
return states;
}
/** Free some parameters.
* @param freeParameters names of the free parameters
*/
private void setFreeParameters(final Iterable<String> freeParameters) {
// start by setting all parameters as not estimated
for (final ParameterDriver driver : builder.getPropagationParametersDrivers().getDrivers()) {
driver.setSelected(false);
}
// set only the selected parameters as estimated
for (final String parameter : freeParameters) {
boolean found = false;
for (final ParameterDriver driver : builder.getPropagationParametersDrivers().getDrivers()) {
if (driver.getName().equals(parameter)) {
found = true;
driver.setSelected(true);
break;
}
}
if (!found) {
// build the list of supported parameters
final StringBuilder sBuilder = new StringBuilder();
for (final ParameterDriver driver : builder.getPropagationParametersDrivers().getDrivers()) {
if (sBuilder.length() > 0) {
sBuilder.append(", ");
}
sBuilder.append(driver.getName());
}
throw new OrekitException(OrekitMessages.UNSUPPORTED_PARAMETER_NAME,
parameter, sBuilder.toString());
}
}
}
/** Adapt a propagator to minimize the mean square error for a set of {@link SpacecraftState states}.
* @param states set of spacecraft states to fit
* @param positionOnly if true, consider only position data otherwise both position and velocity are used
* @return adapted propagator
*/
private Propagator adapt(final List<SpacecraftState> states,
final boolean positionOnly) {
this.onlyPosition = positionOnly;
// very rough first guess using osculating parameters of first sample point
final double[] initial = builder.getSelectedNormalizedParameters();
// warm-up iterations, using only a few points
setSample(states.subList(0, onlyPosition ? 2 : 1));
final double[] intermediate = fit(initial);
// final search using all points
setSample(states);
final double[] result = fit(intermediate);
rms = getRMS(result);
adapted = buildAdaptedPropagator(result);
return adapted;
}
/** Find the propagator that minimize the mean square error for a sample of {@link SpacecraftState states}.
* @param initial initial estimation parameters (position, velocity, free parameters)
* @return fitted parameters
* @exception MathRuntimeException if maximal number of iterations is exceeded
*/
private double[] fit(final double[] initial)
throws MathRuntimeException {
final LeastSquaresProblem problem = new LeastSquaresBuilder().
maxIterations(maxIterations).
maxEvaluations(Integer.MAX_VALUE).
model(getModel()).
target(target).
weight(new DiagonalMatrix(weight)).
start(initial).
checker(checker).
build();
optimum = optimizer.optimize(problem);
return optimum.getPoint().toArray();
}
/** Get the Root Mean Square Deviation for a given parameters set.
* @param parameterSet position/velocity parameters set
* @return RMSD
*/
private double getRMS(final double[] parameterSet) {
final double[] residuals = getObjectiveFunction().value(parameterSet);
for (int i = 0; i < residuals.length; ++i) {
residuals[i] = target[i] - residuals[i];
}
double sum2 = 0;
for (final double residual : residuals) {
sum2 += residual * residual;
}
return FastMath.sqrt(sum2 / residuals.length);
}
/** Build the adpated propagator for a given position/velocity(/free) parameters set.
* @param parameterSet position/velocity(/free) parameters set
* @return adapted propagator
*/
private Propagator buildAdaptedPropagator(final double[] parameterSet) {
return builder.buildPropagator(parameterSet);
}
/** Set the states sample.
* @param states spacecraft states sample
*/
private void setSample(final List<SpacecraftState> states) {
this.sample = states;
if (onlyPosition) {
target = new double[states.size() * 3];
weight = new double[states.size() * 3];
} else {
target = new double[states.size() * 6];
weight = new double[states.size() * 6];
}
int k = 0;
for (int i = 0; i < states.size(); i++) {
final PVCoordinates pv = states.get(i).getPVCoordinates(frame);
// position
target[k] = pv.getPosition().getX();
weight[k++] = 1;
target[k] = pv.getPosition().getY();
weight[k++] = 1;
target[k] = pv.getPosition().getZ();
weight[k++] = 1;
// velocity
if (!onlyPosition) {
// velocity weight relative to position
final double r2 = pv.getPosition().getNormSq();
final double v = pv.getVelocity().getNorm();
final double vWeight = v * r2 / states.get(i).getMu();
target[k] = pv.getVelocity().getX();
weight[k++] = vWeight;
target[k] = pv.getVelocity().getY();
weight[k++] = vWeight;
target[k] = pv.getVelocity().getZ();
weight[k++] = vWeight;
}
}
}
}