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

Example 16 with Orbit

use of org.orekit.orbits.Orbit 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 17 with Orbit

use of org.orekit.orbits.Orbit 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 18 with Orbit

use of org.orekit.orbits.Orbit 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 19 with Orbit

use of org.orekit.orbits.Orbit 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);
}

Example 20 with Orbit

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

the class KalmanOrbitDeterminationTest method testLageos2.

@Test
public // Orbit determination for Lageos2 based on SLR (range) measurements
void testLageos2() throws URISyntaxException, IllegalArgumentException, IOException, OrekitException, ParseException {
    // Print results on console
    final boolean print = false;
    // input in tutorial resources directory/output
    final String inputPath = KalmanOrbitDeterminationTest.class.getClassLoader().getResource("orbit-determination/Lageos2/od_test_Lageos2.in").toURI().getPath();
    final File input = new File(inputPath);
    // configure Orekit data acces
    Utils.setDataRoot("orbit-determination/Lageos2:potential/icgem-format");
    GravityFieldFactory.addPotentialCoefficientsReader(new ICGEMFormatReader("eigen-6s-truncated", true));
    // Choice of an orbit type to use
    // Default for test is Cartesian
    final OrbitType orbitType = OrbitType.CARTESIAN;
    // Initial orbital Cartesian covariance matrix
    // These covariances are derived from the deltas between initial and reference orbits
    // So in a way they are "perfect"...
    // Cartesian covariance matrix initialization
    final RealMatrix cartesianOrbitalP = MatrixUtils.createRealDiagonalMatrix(new double[] { 1e4, 4e3, 1, 5e-3, 6e-5, 1e-4 });
    // Orbital Cartesian process noise matrix (Q)
    final RealMatrix cartesianOrbitalQ = MatrixUtils.createRealDiagonalMatrix(new double[] { 1.e-4, 1.e-4, 1.e-4, 1.e-10, 1.e-10, 1.e-10 });
    // Initial measurement covariance matrix and process noise matrix
    final RealMatrix measurementP = MatrixUtils.createRealDiagonalMatrix(new double[] { 1., 1., 1., 1. });
    final RealMatrix measurementQ = MatrixUtils.createRealDiagonalMatrix(new double[] { 1e-6, 1e-6, 1e-6, 1e-6 });
    // Kalman orbit determination run.
    ResultKalman kalmanLageos2 = run(input, orbitType, print, cartesianOrbitalP, cartesianOrbitalQ, null, null, measurementP, measurementQ);
    // Definition of the accuracy for the test
    final double distanceAccuracy = 0.86;
    final double velocityAccuracy = 4.12e-3;
    // Tests
    // Note: The reference initial orbit is the same as in the batch LS tests
    // -----
    // Number of measurements processed
    final int numberOfMeas = 258;
    Assert.assertEquals(numberOfMeas, kalmanLageos2.getNumberOfMeasurements());
    // Estimated position and velocity
    final Vector3D estimatedPos = kalmanLageos2.getEstimatedPV().getPosition();
    final Vector3D estimatedVel = kalmanLageos2.getEstimatedPV().getVelocity();
    // Reference position and velocity at initial date (same as in batch LS test)
    final Vector3D refPos0 = new Vector3D(-5532131.956902, 10025696.592156, -3578940.040009);
    final Vector3D refVel0 = new Vector3D(-3871.275109, -607.880985, 4280.972530);
    // Run the reference until Kalman last date
    final Orbit refOrbit = runReference(input, orbitType, refPos0, refVel0, null, kalmanLageos2.getEstimatedPV().getDate());
    final Vector3D refPos = refOrbit.getPVCoordinates().getPosition();
    final Vector3D refVel = refOrbit.getPVCoordinates().getVelocity();
    // Check distances
    final double dP = Vector3D.distance(refPos, estimatedPos);
    final double dV = Vector3D.distance(refVel, estimatedVel);
    Assert.assertEquals(0.0, dP, distanceAccuracy);
    Assert.assertEquals(0.0, dV, velocityAccuracy);
    // Print orbit deltas
    if (print) {
        System.out.println("Test performances:");
        System.out.format("\t%-30s\n", "ΔEstimated / Reference");
        System.out.format(Locale.US, "\t%-10s %20.6f\n", "ΔP [m]", dP);
        System.out.format(Locale.US, "\t%-10s %20.6f\n", "ΔV [m/s]", dV);
    }
    // Test on measurements parameters
    final List<DelegatingDriver> list = new ArrayList<DelegatingDriver>();
    list.addAll(kalmanLageos2.measurementsParameters.getDrivers());
    sortParametersChanges(list);
    // Batch LS values
    // final double[] stationOffSet = { 1.659203,  0.861250,  -0.885352 };
    // final double rangeBias = -0.286275;
    final double[] stationOffSet = { 0.298867, -0.137456, 0.013315 };
    final double rangeBias = 0.002390;
    Assert.assertEquals(stationOffSet[0], list.get(0).getValue(), distanceAccuracy);
    Assert.assertEquals(stationOffSet[1], list.get(1).getValue(), distanceAccuracy);
    Assert.assertEquals(stationOffSet[2], list.get(2).getValue(), distanceAccuracy);
    Assert.assertEquals(rangeBias, list.get(3).getValue(), distanceAccuracy);
    // test on statistic for the range residuals
    final long nbRange = 258;
    // Batch LS values
    // final double[] RefStatRange = { -2.431135, 2.218644, 0.038483, 0.982017 };
    final double[] RefStatRange = { -23.561314, 20.436464, 0.964164, 5.687187 };
    Assert.assertEquals(nbRange, kalmanLageos2.getRangeStat().getN());
    Assert.assertEquals(RefStatRange[0], kalmanLageos2.getRangeStat().getMin(), distanceAccuracy);
    Assert.assertEquals(RefStatRange[1], kalmanLageos2.getRangeStat().getMax(), distanceAccuracy);
    Assert.assertEquals(RefStatRange[2], kalmanLageos2.getRangeStat().getMean(), distanceAccuracy);
    Assert.assertEquals(RefStatRange[3], kalmanLageos2.getRangeStat().getStandardDeviation(), distanceAccuracy);
}