Examples with RangeMeasurementCreator org.orekit.estimation.measurements.RangeMeasurementCreator used on opensource projects

Example 6 with RangeMeasurementCreator

use of org.orekit.estimation.measurements.RangeMeasurementCreator in project Orekit by CS-SI.

the class BatchLSEstimatorTest method testWrappedException.

@Test
public void testWrappedException() throws OrekitException {
    Context context = EstimationTestUtils.eccentricContext("regular-data:potential:tides");
    final NumericalPropagatorBuilder propagatorBuilder = context.createBuilder(OrbitType.KEPLERIAN, PositionAngle.TRUE, true, 1.0e-6, 60.0, 1.0);
    // create perfect range measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context), 1.0, 3.0, 300.0);
    // create orbit estimator
    final BatchLSEstimator estimator = new BatchLSEstimator(new LevenbergMarquardtOptimizer(), propagatorBuilder);
    for (final ObservedMeasurement<?> range : measurements) {
        estimator.addMeasurement(range);
    }
    estimator.setParametersConvergenceThreshold(1.0e-2);
    estimator.setMaxIterations(10);
    estimator.setMaxEvaluations(20);
    estimator.setObserver(new BatchLSObserver() {

        /**
         * {@inheritDoc}
         */
        @Override
        public void evaluationPerformed(int iterationsCount, int evaluationscount, Orbit[] orbits, ParameterDriversList estimatedOrbitalParameters, ParameterDriversList estimatedPropagatorParameters, ParameterDriversList estimatedMeasurementsParameters, EstimationsProvider evaluationsProvider, Evaluation lspEvaluation) throws DummyException {
            throw new DummyException();
        }
    });
    try {
        EstimationTestUtils.checkFit(context, estimator, 3, 4, 0.0, 1.5e-6, 0.0, 3.2e-6, 0.0, 3.8e-7, 0.0, 1.5e-10);
        Assert.fail("an exception should have been thrown");
    } catch (DummyException de) {
    // expected
    }
}

Example 7 with RangeMeasurementCreator

use of org.orekit.estimation.measurements.RangeMeasurementCreator in project Orekit by CS-SI.

the class KalmanEstimatorTest method testKeplerianRange.

/**
 * Perfect range measurements with a biased start
 * Keplerian formalism
 * @throws OrekitException
 */
@Test
public void testKeplerianRange() throws OrekitException {
    // Create context
    Context context = EstimationTestUtils.eccentricContext("regular-data:potential:tides");
    // Create initial orbit and propagator builder
    final OrbitType orbitType = OrbitType.KEPLERIAN;
    final PositionAngle positionAngle = PositionAngle.TRUE;
    final boolean perfectStart = true;
    final double minStep = 1.e-6;
    final double maxStep = 60.;
    final double dP = 1.;
    final NumericalPropagatorBuilder propagatorBuilder = context.createBuilder(orbitType, positionAngle, perfectStart, minStep, maxStep, dP);
    // Create perfect range measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context), 1.0, 4.0, 60.0);
    // Reference propagator for estimation performances
    final NumericalPropagator referencePropagator = propagatorBuilder.buildPropagator(propagatorBuilder.getSelectedNormalizedParameters());
    // Reference position/velocity at last measurement date
    final Orbit refOrbit = referencePropagator.propagate(measurements.get(measurements.size() - 1).getDate()).getOrbit();
    // Change semi-major axis of 1.2m as in the batch test
    ParameterDriver aDriver = propagatorBuilder.getOrbitalParametersDrivers().getDrivers().get(0);
    aDriver.setValue(aDriver.getValue() + 1.2);
    aDriver.setReferenceDate(AbsoluteDate.GALILEO_EPOCH);
    // Cartesian covariance matrix initialization
    // 100m on position / 1e-2m/s on velocity
    final RealMatrix cartesianP = MatrixUtils.createRealDiagonalMatrix(new double[] { 100., 100., 100., 1e-2, 1e-2, 1e-2 });
    // Jacobian of the orbital parameters w/r to Cartesian
    final Orbit initialOrbit = orbitType.convertType(context.initialOrbit);
    final double[][] dYdC = new double[6][6];
    initialOrbit.getJacobianWrtCartesian(PositionAngle.TRUE, dYdC);
    final RealMatrix Jac = MatrixUtils.createRealMatrix(dYdC);
    // Keplerian initial covariance matrix
    final RealMatrix initialP = Jac.multiply(cartesianP.multiply(Jac.transpose()));
    // Process noise matrix is set to 0 here
    RealMatrix Q = MatrixUtils.createRealMatrix(6, 6);
    // Build the Kalman filter
    final KalmanEstimatorBuilder kalmanBuilder = new KalmanEstimatorBuilder();
    kalmanBuilder.builder(propagatorBuilder);
    kalmanBuilder.estimatedMeasurementsParameters(new ParameterDriversList());
    kalmanBuilder.initialCovarianceMatrix(initialP);
    kalmanBuilder.processNoiseMatrixProvider(new ConstantProcessNoise(Q));
    final KalmanEstimator kalman = kalmanBuilder.build();
    // Filter the measurements and check the results
    final double expectedDeltaPos = 0.;
    final double posEps = 1.77e-4;
    final double expectedDeltaVel = 0.;
    final double velEps = 7.93e-8;
    final double[] expectedSigmasPos = { 0.742488, 0.281910, 0.563217 };
    final double sigmaPosEps = 1e-6;
    final double[] expectedSigmasVel = { 2.206622e-4, 1.306669e-4, 1.293996e-4 };
    final double sigmaVelEps = 1e-10;
    EstimationTestUtils.checkKalmanFit(context, kalman, measurements, refOrbit, positionAngle, expectedDeltaPos, posEps, expectedDeltaVel, velEps, expectedSigmasPos, sigmaPosEps, expectedSigmasVel, sigmaVelEps);
}

Example 8 with RangeMeasurementCreator

use of org.orekit.estimation.measurements.RangeMeasurementCreator in project Orekit by CS-SI.

the class KalmanEstimatorTest method testWrappedException.

/**
 * Test of a wrapped exception in a Kalman observer
 * @throws OrekitException
 */
@Test
public void testWrappedException() throws OrekitException {
    // Create context
    Context context = EstimationTestUtils.eccentricContext("regular-data:potential:tides");
    // Create initial orbit and propagator builder
    final OrbitType orbitType = OrbitType.KEPLERIAN;
    final PositionAngle positionAngle = PositionAngle.TRUE;
    final boolean perfectStart = true;
    final double minStep = 1.e-6;
    final double maxStep = 60.;
    final double dP = 1.;
    final NumericalPropagatorBuilder propagatorBuilder = context.createBuilder(orbitType, positionAngle, perfectStart, minStep, maxStep, dP);
    // Create perfect range measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context), 1.0, 3.0, 300.0);
    // Build the Kalman filter
    final KalmanEstimatorBuilder kalmanBuilder = new KalmanEstimatorBuilder();
    kalmanBuilder.builder(propagatorBuilder);
    kalmanBuilder.estimatedMeasurementsParameters(new ParameterDriversList());
    kalmanBuilder.initialCovarianceMatrix(MatrixUtils.createRealMatrix(6, 6));
    kalmanBuilder.processNoiseMatrixProvider(new ConstantProcessNoise(MatrixUtils.createRealMatrix(6, 6)));
    final KalmanEstimator kalman = kalmanBuilder.build();
    kalman.setObserver(estimation -> {
        throw new DummyException();
    });
    try {
        // Filter the measurements and expect an exception to occur
        EstimationTestUtils.checkKalmanFit(context, kalman, measurements, context.initialOrbit, positionAngle, 0., 0., 0., 0., new double[3], 0., new double[3], 0.);
    } catch (DummyException de) {
    // expected
    }
}

Example 9 with RangeMeasurementCreator

use of org.orekit.estimation.measurements.RangeMeasurementCreator in project Orekit by CS-SI.

the class BatchLSEstimatorTest method testKeplerRange.

/**
 * Perfect range measurements with a biased start
 * @throws OrekitException
 */
@Test
public void testKeplerRange() throws OrekitException {
    Context context = EstimationTestUtils.eccentricContext("regular-data:potential:tides");
    final NumericalPropagatorBuilder propagatorBuilder = context.createBuilder(OrbitType.KEPLERIAN, PositionAngle.TRUE, true, 1.0e-6, 60.0, 1.0);
    // create perfect range measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context), 1.0, 3.0, 300.0);
    // create orbit estimator
    final BatchLSEstimator estimator = new BatchLSEstimator(new LevenbergMarquardtOptimizer(), propagatorBuilder);
    for (final ObservedMeasurement<?> range : measurements) {
        estimator.addMeasurement(range);
    }
    estimator.setParametersConvergenceThreshold(1.0e-2);
    estimator.setMaxIterations(10);
    estimator.setMaxEvaluations(20);
    estimator.setObserver(new BatchLSObserver() {

        int lastIter = 0;

        int lastEval = 0;

        /**
         * {@inheritDoc}
         */
        @Override
        public void evaluationPerformed(int iterationsCount, int evaluationscount, Orbit[] orbits, ParameterDriversList estimatedOrbitalParameters, ParameterDriversList estimatedPropagatorParameters, ParameterDriversList estimatedMeasurementsParameters, EstimationsProvider evaluationsProvider, Evaluation lspEvaluation) throws OrekitException {
            if (iterationsCount == lastIter) {
                Assert.assertEquals(lastEval + 1, evaluationscount);
            } else {
                Assert.assertEquals(lastIter + 1, iterationsCount);
            }
            lastIter = iterationsCount;
            lastEval = evaluationscount;
            Assert.assertEquals(measurements.size(), evaluationsProvider.getNumber());
            try {
                evaluationsProvider.getEstimatedMeasurement(-1);
                Assert.fail("an exception should have been thrown");
            } catch (OrekitException oe) {
                Assert.assertEquals(LocalizedCoreFormats.OUT_OF_RANGE_SIMPLE, oe.getSpecifier());
            }
            try {
                evaluationsProvider.getEstimatedMeasurement(measurements.size());
                Assert.fail("an exception should have been thrown");
            } catch (OrekitException oe) {
                Assert.assertEquals(LocalizedCoreFormats.OUT_OF_RANGE_SIMPLE, oe.getSpecifier());
            }
            AbsoluteDate previous = AbsoluteDate.PAST_INFINITY;
            for (int i = 0; i < evaluationsProvider.getNumber(); ++i) {
                AbsoluteDate current = evaluationsProvider.getEstimatedMeasurement(i).getDate();
                Assert.assertTrue(current.compareTo(previous) >= 0);
                previous = current;
            }
        }
    });
    ParameterDriver aDriver = estimator.getOrbitalParametersDrivers(true).getDrivers().get(0);
    Assert.assertEquals("a", aDriver.getName());
    aDriver.setValue(aDriver.getValue() + 1.2);
    aDriver.setReferenceDate(AbsoluteDate.GALILEO_EPOCH);
    EstimationTestUtils.checkFit(context, estimator, 2, 3, 0.0, 1.1e-6, 0.0, 2.8e-6, 0.0, 4.0e-7, 0.0, 2.2e-10);
    // got a default one
    for (final ParameterDriver driver : estimator.getOrbitalParametersDrivers(true).getDrivers()) {
        if ("a".equals(driver.getName())) {
            // user-specified reference date
            Assert.assertEquals(0, driver.getReferenceDate().durationFrom(AbsoluteDate.GALILEO_EPOCH), 1.0e-15);
        } else {
            // default reference date
            Assert.assertEquals(0, driver.getReferenceDate().durationFrom(propagatorBuilder.getInitialOrbitDate()), 1.0e-15);
        }
    }
}

Example 10 with RangeMeasurementCreator

use of org.orekit.estimation.measurements.RangeMeasurementCreator in project Orekit by CS-SI.

the class BiasTest method testEstimateBias.

@SuppressWarnings("unchecked")
@Test
public void testEstimateBias() throws OrekitException {
    Context context = EstimationTestUtils.eccentricContext("regular-data:potential:tides");
    final NumericalPropagatorBuilder propagatorBuilder = context.createBuilder(OrbitType.KEPLERIAN, PositionAngle.TRUE, true, 1.0e-6, 60.0, 0.001);
    // create perfect range measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context), 1.0, 3.0, 300.0);
    // create range biases: one bias for each station
    final RandomGenerator random = new Well19937a(0x0c4b69da5d64b35al);
    final Bias<?>[] stationsRangeBiases = new Bias<?>[context.stations.size()];
    final double[] realStationsBiases = new double[context.stations.size()];
    for (int i = 0; i < context.stations.size(); ++i) {
        final TopocentricFrame base = context.stations.get(i).getBaseFrame();
        stationsRangeBiases[i] = new Bias<Range>(new String[] { base.getName() + " range bias" }, new double[] { 0.0 }, new double[] { 1.0 }, new double[] { Double.NEGATIVE_INFINITY }, new double[] { Double.POSITIVE_INFINITY });
        realStationsBiases[i] = 2 * random.nextDouble() - 1;
    }
    // create orbit estimator
    final BatchLSEstimator estimator = new BatchLSEstimator(new LevenbergMarquardtOptimizer(), propagatorBuilder);
    // add the measurements, with both spacecraft and stations biases
    for (final ObservedMeasurement<?> measurement : measurements) {
        final Range range = (Range) measurement;
        for (int i = 0; i < context.stations.size(); ++i) {
            if (range.getStation() == context.stations.get(i)) {
                double biasedRange = range.getObservedValue()[0] + realStationsBiases[i];
                final Range m = new Range(range.getStation(), range.getDate(), biasedRange, range.getTheoreticalStandardDeviation()[0], range.getBaseWeight()[0]);
                m.addModifier((Bias<Range>) stationsRangeBiases[i]);
                estimator.addMeasurement(m);
            }
        }
    }
    estimator.setParametersConvergenceThreshold(1.0e-3);
    estimator.setMaxIterations(10);
    estimator.setMaxEvaluations(20);
    // we want to estimate the biases
    for (Bias<?> bias : stationsRangeBiases) {
        for (final ParameterDriver driver : bias.getParametersDrivers()) {
            driver.setSelected(true);
        }
    }
    EstimationTestUtils.checkFit(context, estimator, 2, 3, 0.0, 7.2e-7, 0.0, 2.1e-6, 0.0, 3.7e-7, 0.0, 1.7e-10);
    for (int i = 0; i < stationsRangeBiases.length; ++i) {
        Assert.assertEquals(realStationsBiases[i], stationsRangeBiases[i].getParametersDrivers().get(0).getValue(), 3.3e-6);
    }
}