AbstractMeasurementBuilder.java
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*
* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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package org.orekit.estimation.measurements.generation;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import org.hipparchus.random.CorrelatedRandomVectorGenerator;
import org.orekit.estimation.measurements.EstimatedMeasurementBase;
import org.orekit.estimation.measurements.EstimationModifier;
import org.orekit.estimation.measurements.ObservableSatellite;
import org.orekit.estimation.measurements.ObservedMeasurement;
import org.orekit.propagation.SpacecraftState;
import org.orekit.propagation.sampling.OrekitStepInterpolator;
import org.orekit.time.AbsoluteDate;
import org.orekit.utils.ParameterDriver;
/** Base class for {@link MeasurementBuilder measurements builders}.
* @param <T> the type of the measurement
* @author Luc Maisonobe
* @since 9.3
*/
public abstract class AbstractMeasurementBuilder<T extends ObservedMeasurement<T>> implements MeasurementBuilder<T> {
/** Noise source (may be null). */
private final CorrelatedRandomVectorGenerator noiseSource;
/** Modifiers that apply to the measurement.*/
private final List<EstimationModifier<T>> modifiers;
/** Theoretical standard deviation. */
private final double[] sigma;
/** Base weight. */
private final double[] baseWeight;
/** Satellites related to this measurement. */
private final ObservableSatellite[] satellites;
/** Start of the measurements time span. */
private AbsoluteDate spanStart;
/** End of the measurements time span. */
private AbsoluteDate spanEnd;
/** Simple constructor.
* @param noiseSource noise source, may be null for generating perfect measurements
* @param sigma theoretical standard deviation
* @param baseWeight base weight
* @param satellites satellites related to this builder
*/
protected AbstractMeasurementBuilder(final CorrelatedRandomVectorGenerator noiseSource,
final double sigma, final double baseWeight,
final ObservableSatellite... satellites) {
this(noiseSource,
new double[] {
sigma
}, new double[] {
baseWeight
}, satellites);
}
/** Simple constructor.
* @param noiseSource noise source, may be null for generating perfect measurements
* @param sigma theoretical standard deviation
* @param baseWeight base weight
* @param satellites satellites related to this builder
*/
protected AbstractMeasurementBuilder(final CorrelatedRandomVectorGenerator noiseSource,
final double[] sigma, final double[] baseWeight,
final ObservableSatellite... satellites) {
this.noiseSource = noiseSource;
this.modifiers = new ArrayList<>();
this.sigma = sigma.clone();
this.baseWeight = baseWeight.clone();
this.satellites = satellites.clone();
}
/** {@inheritDoc}
* <p>
* This implementation stores the time span of the measurements generation.
* </p>
*/
@Override
public void init(final AbsoluteDate start, final AbsoluteDate end) {
spanStart = start;
spanEnd = end;
}
/** {@inheritDoc} */
@Override
public void addModifier(final EstimationModifier<T> modifier) {
modifiers.add(modifier);
}
/** {@inheritDoc} */
@Override
public List<EstimationModifier<T>> getModifiers() {
return Collections.unmodifiableList(modifiers);
}
/** Get the start of the measurements time span.
* @return start of the measurements time span
*/
protected AbsoluteDate getStart() {
return spanStart;
}
/** Get the end of the measurements time span.
* @return end of the measurements time span
*/
protected AbsoluteDate getEnd() {
return spanEnd;
}
/** Generate a noise vector.
* @return noise vector (null if we generate perfect measurements)
*/
protected double[] getNoise() {
return noiseSource == null ? null : noiseSource.nextVector();
}
/** Get the theoretical standard deviation.
* <p>
* The theoretical standard deviation is a theoretical value
* used for normalizing the residuals. It acts as a weighting
* factor to mix appropriately measurements with different units
* and different accuracy. The value has the same dimension as
* the measurement itself (i.e. when a residual is divided by
* this value, it becomes dimensionless).
* </p>
* @return expected standard deviation
* @see #getBaseWeight()
*/
protected double[] getTheoreticalStandardDeviation() {
return sigma.clone();
}
/** Get the base weight associated with the measurement
* <p>
* The base weight is used on residuals already normalized thanks to
* {@link #getTheoreticalStandardDeviation()} to increase or
* decrease relative effect of some measurements with respect to
* other measurements. It is a dimensionless value, typically between
* 0 and 1 (but it can really have any non-negative value).
* </p>
* @return base weight
* @see #getTheoreticalStandardDeviation()
*/
protected double[] getBaseWeight() {
return baseWeight.clone();
}
/** {@inheritDoc} */
@Override
public ObservableSatellite[] getSatellites() {
return satellites.clone();
}
/**
* Build a dummy observed measurement.
*
* @param date measurement date
* @param interpolators interpolators relevant for this builder
* @return dummy observed measurement
* @since 13.0
*/
protected abstract T buildObserved(AbsoluteDate date,
Map<ObservableSatellite, OrekitStepInterpolator> interpolators);
/** {@inheritDoc} */
@Override
public EstimatedMeasurementBase<T> build(final AbsoluteDate date,
final Map<ObservableSatellite, OrekitStepInterpolator> interpolators) {
final SpacecraftState[] relevant = new SpacecraftState[satellites.length];
for (int i = 0; i < relevant.length; ++i) {
relevant[i] = interpolators.get(satellites[i]).getInterpolatedState(date);
}
// create a dummy observed measurement
final T observed = buildObserved(date, interpolators);
for (final EstimationModifier<T> modifier : getModifiers()) {
observed.addModifier(modifier);
}
// set a reference date for parameters missing one
for (final ParameterDriver driver : observed.getParametersDrivers()) {
if (driver.getReferenceDate() == null) {
final AbsoluteDate start = getStart();
final AbsoluteDate end = getEnd();
driver.setReferenceDate(start.durationFrom(end) <= 0 ? start : end);
}
}
// estimate the perfect value of the measurement
final EstimatedMeasurementBase<T> estimated = observed.estimateWithoutDerivatives(relevant);
final double[] value = estimated.getEstimatedValue();
// add the noise
final double[] noise = getNoise();
if (noise != null) {
for (int i = 0; i < value.length; ++i) {
value[i] += noise[i];
}
}
// update the dummy measurement (which is referenced by the estimated measurement)
observed.setObservedValue(value);
return estimated;
}
}