Alfriend1999.java
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*
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package org.orekit.ssa.collision.shorttermencounter.probability.twod;
import org.hipparchus.CalculusFieldElement;
import org.hipparchus.util.FastMath;
/**
* Compute the probability of collision using the method described in : "Kyle Alfriend, Maruthi Akella, Joseph Frisbee, James
* Foster, Deok-Jin Lee, and Matthew Wilkins. Probability of ProbabilityOfCollision Error Analysis. Space Debris, 1(1):21–35,
* 1999.".
* <p>It assumes :
* <ul>
* <li>Short encounter leading to a linear relative motion.</li>
* <li>Spherical collision object.</li>
* <li>Uncorrelated positional covariance.</li>
* <li>Gaussian distribution of the position uncertainties.</li>
* <li>Deterministic velocity i.e. no velocity uncertainties.</li>
* <li>Both objects are in circular orbits (eq 14).</li>
* <li>Probability density function is constant over the collision disk (eq 18).</li>
* </ul>
* <p>
* By assuming a constant probability density function over the collision circle this method will,
* <b>most of the time</b>, give much higher probability of collision than other regular methods.
* That is why it is qualified as a maximum probability of collision computing method.</p>
*
* @author Vincent Cucchietti
* @since 12.0
*/
public class Alfriend1999 extends AbstractAlfriend1999 {
/** Empty constructor. */
public Alfriend1999() {
super(ShortTermEncounter2DPOCMethodType.ALFRIEND_1999.name());
}
/** {@inheritDoc} */
@Override
public boolean isAMaximumProbabilityOfCollisionMethod() {
return true;
}
/** {@inheritDoc} */
@Override
public ShortTermEncounter2DPOCMethodType getType() {
return ShortTermEncounter2DPOCMethodType.ALFRIEND_1999;
}
/** {@inheritDoc} */
@Override
double computeValue(final double radius, final double squaredMahalanobisDistance,
final double covarianceMatrixDeterminant) {
return FastMath.exp(-0.5 * squaredMahalanobisDistance) * radius * radius /
(2 * FastMath.sqrt(covarianceMatrixDeterminant));
}
/** {@inheritDoc} */
@Override
<T extends CalculusFieldElement<T>> T computeValue(final T radius, final T squaredMahalanobisDistance,
final T covarianceMatrixDeterminant) {
return squaredMahalanobisDistance.multiply(-0.5).exp().multiply(radius.square())
.divide(covarianceMatrixDeterminant.sqrt().multiply(2.));
}
}