Examples with PositionAngle org.orekit.orbits.PositionAngle used on opensource projects

Example 1 with PositionAngle

use of org.orekit.orbits.PositionAngle in project Orekit by CS-SI.

the class OrbitDeterminationTest method createOrbit.

/**
 * Create an orbit from input parameters
 * @param parser input file parser
 * @param mu     central attraction coefficient
 * @throws NoSuchElementException if input parameters are missing
 * @throws OrekitException if inertial frame cannot be created
 */
private Orbit createOrbit(final KeyValueFileParser<ParameterKey> parser, final double mu) throws NoSuchElementException, OrekitException {
    final Frame frame;
    if (!parser.containsKey(ParameterKey.INERTIAL_FRAME)) {
        frame = FramesFactory.getEME2000();
    } else {
        frame = parser.getInertialFrame(ParameterKey.INERTIAL_FRAME);
    }
    // Orbit definition
    PositionAngle angleType = PositionAngle.MEAN;
    if (parser.containsKey(ParameterKey.ORBIT_ANGLE_TYPE)) {
        angleType = PositionAngle.valueOf(parser.getString(ParameterKey.ORBIT_ANGLE_TYPE).toUpperCase());
    }
    if (parser.containsKey(ParameterKey.ORBIT_KEPLERIAN_A)) {
        return new KeplerianOrbit(parser.getDouble(ParameterKey.ORBIT_KEPLERIAN_A), parser.getDouble(ParameterKey.ORBIT_KEPLERIAN_E), parser.getAngle(ParameterKey.ORBIT_KEPLERIAN_I), parser.getAngle(ParameterKey.ORBIT_KEPLERIAN_PA), parser.getAngle(ParameterKey.ORBIT_KEPLERIAN_RAAN), parser.getAngle(ParameterKey.ORBIT_KEPLERIAN_ANOMALY), angleType, frame, parser.getDate(ParameterKey.ORBIT_DATE, TimeScalesFactory.getUTC()), mu);
    } else if (parser.containsKey(ParameterKey.ORBIT_EQUINOCTIAL_A)) {
        return new EquinoctialOrbit(parser.getDouble(ParameterKey.ORBIT_EQUINOCTIAL_A), parser.getDouble(ParameterKey.ORBIT_EQUINOCTIAL_EX), parser.getDouble(ParameterKey.ORBIT_EQUINOCTIAL_EY), parser.getDouble(ParameterKey.ORBIT_EQUINOCTIAL_HX), parser.getDouble(ParameterKey.ORBIT_EQUINOCTIAL_HY), parser.getAngle(ParameterKey.ORBIT_EQUINOCTIAL_LAMBDA), angleType, frame, parser.getDate(ParameterKey.ORBIT_DATE, TimeScalesFactory.getUTC()), mu);
    } else if (parser.containsKey(ParameterKey.ORBIT_CIRCULAR_A)) {
        return new CircularOrbit(parser.getDouble(ParameterKey.ORBIT_CIRCULAR_A), parser.getDouble(ParameterKey.ORBIT_CIRCULAR_EX), parser.getDouble(ParameterKey.ORBIT_CIRCULAR_EY), parser.getAngle(ParameterKey.ORBIT_CIRCULAR_I), parser.getAngle(ParameterKey.ORBIT_CIRCULAR_RAAN), parser.getAngle(ParameterKey.ORBIT_CIRCULAR_ALPHA), angleType, frame, parser.getDate(ParameterKey.ORBIT_DATE, TimeScalesFactory.getUTC()), mu);
    } else if (parser.containsKey(ParameterKey.ORBIT_TLE_LINE_1)) {
        final String line1 = parser.getString(ParameterKey.ORBIT_TLE_LINE_1);
        final String line2 = parser.getString(ParameterKey.ORBIT_TLE_LINE_2);
        final TLE tle = new TLE(line1, line2);
        TLEPropagator propagator = TLEPropagator.selectExtrapolator(tle);
        // propagator.setEphemerisMode();
        AbsoluteDate initDate = tle.getDate();
        SpacecraftState initialState = propagator.getInitialState();
        // Transformation from TEME to frame.
        Transform t = FramesFactory.getTEME().getTransformTo(FramesFactory.getEME2000(), initDate.getDate());
        return new CartesianOrbit(t.transformPVCoordinates(initialState.getPVCoordinates()), frame, initDate, mu);
    } else {
        final double[] pos = { parser.getDouble(ParameterKey.ORBIT_CARTESIAN_PX), parser.getDouble(ParameterKey.ORBIT_CARTESIAN_PY), parser.getDouble(ParameterKey.ORBIT_CARTESIAN_PZ) };
        final double[] vel = { parser.getDouble(ParameterKey.ORBIT_CARTESIAN_VX), parser.getDouble(ParameterKey.ORBIT_CARTESIAN_VY), parser.getDouble(ParameterKey.ORBIT_CARTESIAN_VZ) };
        return new CartesianOrbit(new PVCoordinates(new Vector3D(pos), new Vector3D(vel)), frame, parser.getDate(ParameterKey.ORBIT_DATE, TimeScalesFactory.getUTC()), mu);
    }
}

Example 2 with PositionAngle

use of org.orekit.orbits.PositionAngle in project Orekit by CS-SI.

the class KalmanEstimatorTest method testKeplerianRangeAzElAndRangeRate.

/**
 * Perfect Range, Azel and range rate measurements with a biased start
 *  Start: position/velocity biased with: [+1000,0,0] m and [0,0,0.01] m/s
 *  Keplerian formalism
 */
@Test
public void testKeplerianRangeAzElAndRangeRate() 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 measPropagatorBuilder = context.createBuilder(orbitType, positionAngle, perfectStart, minStep, maxStep, dP);
    // Create perfect range measurements
    final Propagator rangePropagator = EstimationTestUtils.createPropagator(context.initialOrbit, measPropagatorBuilder);
    final List<ObservedMeasurement<?>> rangeMeasurements = EstimationTestUtils.createMeasurements(rangePropagator, new RangeMeasurementCreator(context), 0.0, 4.0, 300.0);
    // Create perfect az/el measurements
    final Propagator angularPropagator = EstimationTestUtils.createPropagator(context.initialOrbit, measPropagatorBuilder);
    final List<ObservedMeasurement<?>> angularMeasurements = EstimationTestUtils.createMeasurements(angularPropagator, new AngularAzElMeasurementCreator(context), 0.0, 4.0, 500.0);
    // Create perfect range rate measurements
    final Propagator rangeRatePropagator = EstimationTestUtils.createPropagator(context.initialOrbit, measPropagatorBuilder);
    final List<ObservedMeasurement<?>> rangeRateMeasurements = EstimationTestUtils.createMeasurements(rangeRatePropagator, new RangeRateMeasurementCreator(context, false), 0.0, 4.0, 700.0);
    // Concatenate measurements
    final List<ObservedMeasurement<?>> measurements = new ArrayList<ObservedMeasurement<?>>();
    measurements.addAll(rangeMeasurements);
    measurements.addAll(angularMeasurements);
    measurements.addAll(rangeRateMeasurements);
    measurements.sort(new ChronologicalComparator());
    // Reference propagator for estimation performances
    final NumericalPropagator referencePropagator = measPropagatorBuilder.buildPropagator(measPropagatorBuilder.getSelectedNormalizedParameters());
    // Reference position/velocity at last measurement date
    final Orbit refOrbit = referencePropagator.propagate(measurements.get(measurements.size() - 1).getDate()).getOrbit();
    // Biased propagator for the Kalman
    final NumericalPropagatorBuilder propagatorBuilder = context.createBuilder(orbitType, positionAngle, false, minStep, maxStep, dP);
    // Cartesian covariance matrix initialization
    // Initial sigmas: 1000m on position, 0.01m/s on velocity
    final RealMatrix cartesianP = MatrixUtils.createRealDiagonalMatrix(new double[] { 1e6, 1e6, 1e6, 1e-4, 1e-4, 1e-4 });
    // 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, dYdC);
    final RealMatrix Jac = MatrixUtils.createRealMatrix(dYdC);
    // Orbital initial covariance matrix
    final RealMatrix initialP = Jac.multiply(cartesianP.multiply(Jac.transpose()));
    // Process noise matrix
    final RealMatrix cartesianQ = MatrixUtils.createRealDiagonalMatrix(new double[] { 1.e-4, 1.e-4, 1.e-4, 1.e-10, 1.e-10, 1.e-10 });
    final RealMatrix Q = Jac.multiply(cartesianQ.multiply(Jac.transpose()));
    // 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 = 2.91e-2;
    final double expectedDeltaVel = 0.;
    final double velEps = 5.52e-6;
    final double[] expectedSigmasPos = { 1.747570, 0.666879, 1.696182 };
    final double sigmaPosEps = 1e-6;
    final double[] expectedSigmasVel = { 5.413666e-4, 4.088359e-4, 4.315316e-4 };
    final double sigmaVelEps = 1e-10;
    EstimationTestUtils.checkKalmanFit(context, kalman, measurements, refOrbit, positionAngle, expectedDeltaPos, posEps, expectedDeltaVel, velEps, expectedSigmasPos, sigmaPosEps, expectedSigmasVel, sigmaVelEps);
}

Example 3 with PositionAngle

use of org.orekit.orbits.PositionAngle in project Orekit by CS-SI.

the class KalmanEstimatorTest method testKeplerianRangeWithOnBoardAntennaOffset.

/**
 * Perfect range measurements with a biased start and an on-board antenna range offset
 * Keplerian formalism
 * @throws OrekitException
 */
@Test
public void testKeplerianRangeWithOnBoardAntennaOffset() 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);
    propagatorBuilder.setAttitudeProvider(new LofOffset(propagatorBuilder.getFrame(), LOFType.LVLH));
    // Antenna phase center definition
    final Vector3D antennaPhaseCenter = new Vector3D(-1.2, 2.3, -0.7);
    // Create perfect range measurements with antenna offset
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context, antennaPhaseCenter), 1.0, 3.0, 300.0);
    // Add antenna offset to the measurements
    final OnBoardAntennaRangeModifier obaModifier = new OnBoardAntennaRangeModifier(antennaPhaseCenter);
    for (final ObservedMeasurement<?> range : measurements) {
        ((Range) range).addModifier(obaModifier);
    }
    // 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[] { 10., 10., 10., 1e-3, 1e-3, 1e-3 });
    // Jacobian of the orbital parameters w/r to Cartesian
    final Orbit initialOrbit = OrbitType.KEPLERIAN.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 = 4.57e-3;
    final double expectedDeltaVel = 0.;
    final double velEps = 7.29e-6;
    final double[] expectedSigmasPos = { 1.105194, 0.930785, 1.254579 };
    final double sigmaPosEps = 1e-6;
    final double[] expectedSigmasVel = { 6.193718e-4, 4.088774e-4, 3.299135e-4 };
    final double sigmaVelEps = 1e-10;
    EstimationTestUtils.checkKalmanFit(context, kalman, measurements, refOrbit, positionAngle, expectedDeltaPos, posEps, expectedDeltaVel, velEps, expectedSigmasPos, sigmaPosEps, expectedSigmasVel, sigmaVelEps);
}

Example 4 with PositionAngle

use of org.orekit.orbits.PositionAngle in project Orekit by CS-SI.

the class KalmanEstimatorTest method testKeplerianRangeAndRangeRate.

/**
 * Perfect range and range rate measurements with a perfect start
 * @throws OrekitException
 */
@Test
public void testKeplerianRangeAndRangeRate() 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 & range rate measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurementsRange = EstimationTestUtils.createMeasurements(propagator, new RangeMeasurementCreator(context), 1.0, 3.0, 300.0);
    final List<ObservedMeasurement<?>> measurementsRangeRate = EstimationTestUtils.createMeasurements(propagator, new RangeRateMeasurementCreator(context, false), 1.0, 3.0, 300.0);
    // Concatenate measurements
    final List<ObservedMeasurement<?>> measurements = new ArrayList<ObservedMeasurement<?>>();
    measurements.addAll(measurementsRange);
    measurements.addAll(measurementsRangeRate);
    // 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();
    // Cartesian covariance matrix initialization
    // 100m on position / 1e-2m/s on velocity
    final RealMatrix cartesianP = MatrixUtils.createRealDiagonalMatrix(new double[] { 1e-2, 1e-2, 1e-2, 1e-8, 1e-8, 1e-8 });
    // 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
    final RealMatrix cartesianQ = MatrixUtils.createRealDiagonalMatrix(new double[] { 1.e-4, 1.e-4, 1.e-4, 1.e-10, 1.e-10, 1.e-10 });
    final RealMatrix Q = Jac.multiply(cartesianQ.multiply(Jac.transpose()));
    // 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 = 5.96e-3;
    final double expectedDeltaVel = 0.;
    final double velEps = 2.06e-6;
    final double[] expectedSigmasPos = { 0.341538, 8.175281, 4.634384 };
    final double sigmaPosEps = 1e-6;
    final double[] expectedSigmasVel = { 1.167838e-3, 1.036437e-3, 2.834385e-3 };
    final double sigmaVelEps = 1e-9;
    EstimationTestUtils.checkKalmanFit(context, kalman, measurements, refOrbit, positionAngle, expectedDeltaPos, posEps, expectedDeltaVel, velEps, expectedSigmasPos, sigmaPosEps, expectedSigmasVel, sigmaVelEps);
}

Example 5 with PositionAngle

use of org.orekit.orbits.PositionAngle in project Orekit by CS-SI.

the class KalmanEstimatorTest method testKeplerianPV.

/**
 * Perfect PV measurements with a perfect start
 * Keplerian formalism
 * @throws OrekitException
 */
@Test
public void testKeplerianPV() 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 PV measurements
    final Propagator propagator = EstimationTestUtils.createPropagator(context.initialOrbit, propagatorBuilder);
    final List<ObservedMeasurement<?>> measurements = EstimationTestUtils.createMeasurements(propagator, new PVMeasurementCreator(), 0.0, 3.0, 300.0);
    // 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();
    // Covariance matrix initialization
    final RealMatrix initialP = MatrixUtils.createRealDiagonalMatrix(new double[] { 1e-2, 1e-2, 1e-2, 1e-5, 1e-5, 1e-5 });
    // Process noise matrix
    RealMatrix Q = MatrixUtils.createRealDiagonalMatrix(new double[] { 1.e-8, 1.e-8, 1.e-8, 1.e-8, 1.e-8, 1.e-8 });
    // 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 = 5.80e-8;
    final double expectedDeltaVel = 0.;
    final double velEps = 2.28e-11;
    final double[] expectedsigmasPos = { 0.998872, 0.933655, 0.997516 };
    final double sigmaPosEps = 1e-6;
    final double[] expectedSigmasVel = { 9.478853e-4, 9.910788e-4, 5.0438709e-4 };
    final double sigmaVelEps = 1e-10;
    EstimationTestUtils.checkKalmanFit(context, kalman, measurements, refOrbit, positionAngle, expectedDeltaPos, posEps, expectedDeltaVel, velEps, expectedsigmasPos, sigmaPosEps, expectedSigmasVel, sigmaVelEps);
}