SequentialBatchLSEstimator.java
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package org.orekit.estimation.leastsquares;
import org.hipparchus.linear.MatrixDecomposer;
import org.hipparchus.linear.QRDecomposer;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation;
import org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer;
import org.orekit.propagation.conversion.OrbitDeterminationPropagatorBuilder;
import org.orekit.propagation.conversion.PropagatorBuilder;
/**
* Sequential least squares estimator for orbit determination.
* <p>
* When an orbit has already been estimated and new measurements are given, it is not efficient
* to re-optimize the whole problem. Only considering the new measures while optimizing
* will neither give good results as the old measurements will not be taken into account.
* Thus, a sequential estimator is used to estimate the orbit, which uses the old results
* of the estimation and the new measurements.
* <p>
* In order to perform a sequential optimization, the user must configure a
* {@link org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer SequentialGaussNewtonOptimizer}.
* Depending if its input data are an empty {@link Evaluation}, a complete <code>Evaluation</code>
* or an a priori state and covariance, different configuration are possible.
* <p>
* <b>1. No input data from a previous estimation</b>
* <p>
* Then, the {@link SequentialBatchLSEstimator} can be used like a {@link BatchLSEstimator}
* to perform the estimation. The user can initialize the <code>SequentialGaussNewtonOptimizer</code>
* using the default constructor.
* <p>
* <code>final SequentialGaussNewtonOptimizer optimizer = new SequentialGaussNewtonOptimizer();</code>
* <p>
* By default, a {@link QRDecomposer} is used as decomposition algorithm. In addition, normal
* equations are not form. It is possible to update these two default configurations by using:
* <ul>
* <li>{@link org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer#withDecomposer(MatrixDecomposer) withDecomposer} method:
* <code>optimizer.withDecomposer(newDecomposer);</code>
* </li>
* <li>{@link org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer#withFormNormalEquations(boolean) withFormNormalEquations} method:
* <code>optimizer.withFormNormalEquations(newFormNormalEquations);</code>
* </li>
* </ul>
* <p>
* <b>2. Initialization using a previous <code>Evalutation</code></b>
* <p>
* In this situation, it is recommended to use the second constructor of the optimizer class.
* <p>
* <code>final SequentialGaussNewtonOptimizer optimizer = new SequentialGaussNewtonOptimizer(decomposer,
* formNormalEquations,
* evaluation);
* </code>
* <p>
* Using this constructor, the user can directly configure the MatrixDecomposer and set the flag for normal equations
* without calling the two previous presented methods.
* <p>
* <i>Note:</i> This constructor can also be used to perform the initialization of <b>1.</b>
* In this case, the <code>Evaluation evaluation</code> is <code>null</code>.
* <p>
* <b>3. Initialization using an a priori estimated state and covariance</b>
* <p>
* These situation is a classical satellite operation need. Indeed, a classical action is to use
* the results of a previous orbit determination (estimated state and covariance) performed a day before,
* to improve the initialization and the results of an orbit determination performed the current day.
* In this situation, the user can initialize the <code>SequentialGaussNewtonOptimizer</code>
* using the default constructor.
* <p>
* <code>final SequentialGaussNewtonOptimizer optimizer = new SequentialGaussNewtonOptimizer();</code>
* <p>
* The MatrixDecomposer and the flag about normal equations can again be updated using the two previous
* presented methods. The a priori state and covariance matrix can be set using:
* <ul>
* <li>{@link org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer#withAPrioriData(org.hipparchus.linear.RealVector, org.hipparchus.linear.RealMatrix) withAPrioriData} method:
* <code>optimizer.withAPrioriData(aPrioriState, aPrioriCovariance);</code>
* </li>
* </ul>
* @author Julie Bayard
* @since 11.0
*/
public class SequentialBatchLSEstimator extends BatchLSEstimator {
/**
* Simple constructor.
* <p>
* If multiple {@link PropagatorBuilder propagator builders} are set up, the
* orbits of several spacecrafts will be used simultaneously. This is useful
* if the propagators share some model or measurements parameters (typically
* pole motion, prime meridian correction or ground stations positions).
* </p>
* <p>
* Setting up multiple {@link PropagatorBuilder propagator builders} is also
* useful when inter-satellite measurements are used, even if only one of
* the orbit is estimated and the other ones are fixed. This is typically
* used when very high accuracy GNSS measurements are needed and the
* navigation bulletins are not considered accurate enough and the
* navigation constellation must be propagated numerically.
* </p>
* <p>
* The solver used for sequential least squares problem is a
* {@link org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer
* sequential Gauss Newton optimizer}.
* Details about how initialize it are given in the class JavaDoc.
* </p>
*
* @param sequentialOptimizer solver for sequential least squares problem
* @param propagatorBuilder builders to use for propagation.
*/
public SequentialBatchLSEstimator(final SequentialGaussNewtonOptimizer sequentialOptimizer,
final OrbitDeterminationPropagatorBuilder... propagatorBuilder) {
super(sequentialOptimizer, propagatorBuilder);
}
}