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/* 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,
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* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.orekit.propagation;
import org.hipparchus.analysis.polynomials.SmoothStepFactory;
import org.hipparchus.linear.RealMatrix;
import org.orekit.frames.Frame;
import org.orekit.frames.LOFType;
import org.orekit.orbits.Orbit;
import org.orekit.orbits.OrbitType;
import org.orekit.orbits.PositionAngleType;
import org.orekit.time.AbsoluteDate;
import org.orekit.time.TimeInterpolator;
import org.orekit.time.TimeStampedPair;
import java.util.List;
/**
* State covariance blender.
*
* Its purpose is to interpolate state covariance between tabulated state covariances by using the concept of blending,
* exposed in : "Efficient Covariance Interpolation using Blending of Approximate State Error Transitions" by Sergei
* Tanygin.
*
* It propagates tabulated values to the interpolation date assuming a standard keplerian model and then blend each
* propagated covariances using a smoothstep function.
*
* It gives accurate results as explained here. In the
* very poorly tracked test case evolving in a highly dynamical environment mentioned in the linked thread, the user can
* expect at worst errors of less than 0.25% in position sigmas and less than 0.4% in velocity sigmas with steps of 40mn
* between tabulated values.
*
* @author Vincent Cucchietti
* @see org.hipparchus.analysis.polynomials.SmoothStepFactory
* @see org.hipparchus.analysis.polynomials.SmoothStepFactory.SmoothStepFunction
*/
public class StateCovarianceBlender extends AbstractStateCovarianceInterpolator {
/** Blending function. */
private final SmoothStepFactory.SmoothStepFunction blendingFunction;
/**
* Constructor.
*
* BEWARE: If the output local orbital frame is not considered pseudo-inertial, all the covariance components
* related to the velocity will be poorly interpolated. Only the position covariance should be considered in this
* case.
*
* @param blendingFunction blending function
* @param orbitInterpolator orbit interpolator
* @param outLOF local orbital frame
*
* @see Frame
* @see OrbitType
* @see PositionAngleType
*/
public StateCovarianceBlender(final SmoothStepFactory.SmoothStepFunction blendingFunction,
final TimeInterpolator orbitInterpolator,
final LOFType outLOF) {
super(DEFAULT_INTERPOLATION_POINTS, 0., orbitInterpolator, outLOF);
this.blendingFunction = blendingFunction;
}
/**
* Constructor.
*
* @param blendingFunction blending function
* @param orbitInterpolator orbit interpolator
* @param outFrame desired output covariance frame
* @param outPositionAngleType desired output position angle
* @param outOrbitType desired output orbit type
*
* @see Frame
* @see OrbitType
* @see PositionAngleType
*/
public StateCovarianceBlender(final SmoothStepFactory.SmoothStepFunction blendingFunction,
final TimeInterpolator orbitInterpolator,
final Frame outFrame,
final OrbitType outOrbitType,
final PositionAngleType outPositionAngleType) {
super(DEFAULT_INTERPOLATION_POINTS, 0., orbitInterpolator, outFrame, outOrbitType, outPositionAngleType);
this.blendingFunction = blendingFunction;
}
/** {@inheritDoc} */
@Override
protected StateCovariance computeInterpolatedCovarianceInOrbitFrame(
final List> uncertainStates,
final Orbit interpolatedOrbit) {
// Necessarily only two sample for blending
final TimeStampedPair previousUncertainState = uncertainStates.get(0);
final TimeStampedPair nextUncertainState = uncertainStates.get(1);
// Get the interpolation date
final AbsoluteDate interpolationDate = interpolatedOrbit.getDate();
// Propagate previous tabulated covariance to interpolation date
final StateCovariance forwardedCovariance =
propagateCovarianceAnalytically(interpolationDate, interpolatedOrbit, previousUncertainState);
// Propagate next tabulated covariance to interpolation date
final StateCovariance backwardedCovariance =
propagateCovarianceAnalytically(interpolationDate, interpolatedOrbit, nextUncertainState);
// Compute normalized time parameter
final double timeParameter =
getTimeParameter(interpolationDate, previousUncertainState.getDate(), nextUncertainState.getDate());
// Blend the covariance matrices
final double blendingValue = blendingFunction.value(timeParameter);
final RealMatrix forwardedCovarianceMatrix = forwardedCovariance.getMatrix();
final RealMatrix backwardedCovarianceMatrix = backwardedCovariance.getMatrix();
final RealMatrix blendedCovarianceMatrix =
forwardedCovarianceMatrix.blendArithmeticallyWith(backwardedCovarianceMatrix, blendingValue);
return new StateCovariance(blendedCovarianceMatrix, interpolationDate, interpolatedOrbit.getFrame(),
OrbitType.CARTESIAN, DEFAULT_POSITION_ANGLE);
}
/**
* Propagate given state covariance to the interpolation date using keplerian motion.
*
* @param interpolationTime interpolation date at which we desire to interpolate the state covariance
* @param orbitAtInterpolatingTime orbit at interpolation date
* @param tabulatedUncertainState tabulated uncertain state
*
* @return state covariance at given interpolation date.
*
* @see StateCovariance
*/
private StateCovariance propagateCovarianceAnalytically(final AbsoluteDate interpolationTime,
final Orbit orbitAtInterpolatingTime,
final TimeStampedPair tabulatedUncertainState) {
// Get orbit and covariance
final Orbit tabulatedOrbit = tabulatedUncertainState.getFirst();
final StateCovariance tabulatedCovariance = tabulatedUncertainState.getSecond();
final Frame interpolatedOrbitFrame = orbitAtInterpolatingTime.getFrame();
// Express tabulated covariance in interpolated orbit frame for consistency
final StateCovariance tabulatedCovarianceInOrbitFrame =
tabulatedCovariance.changeCovarianceFrame(tabulatedOrbit, interpolatedOrbitFrame);
// First convert the covariance matrix to equinoctial elements to avoid singularities inherent to keplerian elements
final RealMatrix covarianceMatrixInEquinoctial =
tabulatedCovarianceInOrbitFrame.changeCovarianceType(tabulatedOrbit, OrbitType.EQUINOCTIAL,
DEFAULT_POSITION_ANGLE).getMatrix();
// Compute state error transition matrix in equinoctial elements (identical to the one in keplerian elements)
final RealMatrix stateErrorTransitionMatrixInEquinoctial =
StateCovariance.getStm(tabulatedOrbit, interpolationTime.durationFrom(tabulatedOrbit.getDate()));
// Propagate the covariance matrix using the previously computed state error transition matrix
final RealMatrix propagatedCovarianceMatrixInEquinoctial =
stateErrorTransitionMatrixInEquinoctial.multiply(
covarianceMatrixInEquinoctial.multiplyTransposed(stateErrorTransitionMatrixInEquinoctial));
// Recreate a StateCovariance instance
final StateCovariance propagatedCovarianceInEquinoctial =
new StateCovariance(propagatedCovarianceMatrixInEquinoctial, interpolationTime,
interpolatedOrbitFrame, OrbitType.EQUINOCTIAL, DEFAULT_POSITION_ANGLE);
// Output propagated state covariance after converting back to cartesian elements
return propagatedCovarianceInEquinoctial.changeCovarianceType(orbitAtInterpolatingTime,
OrbitType.CARTESIAN, DEFAULT_POSITION_ANGLE);
}
}