{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Orbit determination example\n", "This notebook does the following:\n", "* Download an orbit first guess from SpaceTrack\n", "* Download laser ranging data\n", "* Feed the data to Orekit\n", "* Estimate the orbit\n", "* Propagate and compare the orbit to the first guess" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two types of laser ranging data can be chosen (see below):\n", "\n", "* Normal point data: https://ilrs.cddis.eosdis.nasa.gov/data_and_products/data/npt/index.html\n", "* Full rate data: https://ilrs.cddis.eosdis.nasa.gov/data_and_products/data/frt/index.html\n", " * This will improve the orbit estimation\n", " * Caution, this format involves large quantities of data\n", " * Caution 2, this data is unfiltered, therefore there can be a superposition of two range curves if two retro-reflectors on the satellite are visible by the station at the same time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OD parameters\n", "First, some parameters need to be defined for the orbit determination:\n", "* Satellite ID in NORAD and COSPAR format\n", "* Spacecraft mass: important for the drag term\n", "* Measurement weights: used to weight certain measurements more than others during the orbit estimation. Here, we only have range measurements and we do not know the confidence associated to these measurements, so all weights are identical\n", "* OD date: date at which the orbit will be estimated. \n", "* Data collection duration: for example, if equals 2 days, the laser data from the 2 days before the OD date will be used to estimate the orbit. This value is an important trade-off for the quality of the orbit determination:\n", " * The longer the duration, the more ranging data is available, which can increase the quality of the estimation\n", " * The longer the duration, the longer the orbit must be propagated, and the higher the covariance because of the orbit perturbations such as the gravity field, drag, Sun, Moon, etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Satellite parameters" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "sat_list = { \n", " 'envisat': {\n", " 'norad_id': 27386,\n", " 'cospar_id': '0200901',\n", " 'sic_id': '6179',\n", " 'mass': 8000.0, # kg; TODO: compute proper value\n", " 'cross_section': 100.0, # m2; TODO: compute proper value\n", " 'cd': 2.0, # TODO: compute proper value\n", " 'cr': 1.0 # TODO: compute proper value\n", " }, \n", " 'lageos2': {\n", " 'norad_id': 22195,\n", " 'cospar_id': '9207002',\n", " 'sic_id': '5986',\n", " 'mass': 405.0, # kg\n", " 'cross_section': 0.2827, # m2\n", " 'cd': 2.0, # TODO: compute proper value\n", " 'cr': 1.0 # TODO: compute proper value\n", " },\n", " 'technosat': {\n", " 'norad_id': 42829,\n", " 'cospar_id': '1704205',\n", " 'sic_id': '6203',\n", " 'mass': 20.0, # kg\n", " 'cross_section': 0.10, # m2,\n", " 'cd': 2.0, # TODO: compute proper value\n", " 'cr': 1.0 # TODO: compute proper value\n", " }\n", "}\n", "\n", "sc_name = 'lageos2' # Change the name to select a different satellite in the dict" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Constants\n", "c = 299792458 # m/s\n", "\n", "# Parameters\n", "range_weight = 1.0 # Will be normalized later (i.e divided by the number of observations)\n", "range_sigma = 1.0 # Estimated covariance of the range measurements, in meters\n", "\n", "from datetime import datetime\n", "odDate = datetime(2018, 9, 22) # Beginning of the orbit determination\n", "collectionDuration = 2 # days\n", "from datetime import timedelta\n", "startCollectionDate = odDate + timedelta(days=-collectionDuration)\n", "\n", "# Orbit propagator parameters\n", "prop_min_step = 0.001 # s\n", "prop_max_step = 300.0 # s\n", "prop_position_error = 10.0 # m\n", "\n", "# Estimator parameters\n", "estimator_position_scale = 1.0 # m\n", "estimator_convergence_thres = 1e-3\n", "estimator_max_iterations = 25\n", "estimator_max_evaluations = 35" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## API credentials\n", "The following sets up accounts for SpaceTrack (for orbit data) and the EDC API (for laser ranging data).\n", "* A SpaceTrack account is required, it can be created for free at: https://www.space-track.org/auth/createAccount\n", "* An EDC account is required, it can be created for free at: https://edc.dgfi.tum.de/en/register/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "Enter SpaceTrack username clement@jonglez.space\n", "Enter SpaceTrack password for account clement@jonglez.space ·····················\n", "Enter EDC API username jonglez\n", "Enter EDC API password for account jonglez ·······\n" ] } ], "source": [ "# Space-Track\n", "identity_st = input('Enter SpaceTrack username')\n", "import getpass\n", "password_st = getpass.getpass(prompt='Enter SpaceTrack password for account {}'.format(identity_st))\n", "import spacetrack.operators as op\n", "from spacetrack import SpaceTrackClient\n", "st = SpaceTrackClient(identity=identity_st, password=password_st)\n", "\n", "# EDC API\n", "username_edc = input('Enter EDC API username')\n", "password_edc = getpass.getpass(prompt='Enter EDC API password for account {}'.format(username_edc)) # You will get prompted for your password\n", "url = 'https://edc.dgfi.tum.de/api/v1/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up models\n", "Initializing Orekit and JVM" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import orekit\n", "orekit.initVM()\n", "\n", "# Modified from https://gitlab.orekit.org/orekit-labs/python-wrapper/blob/master/python_files/pyhelpers.py\n", "from java.io import File\n", "from org.orekit.data import DataProvidersManager, DirectoryCrawler\n", "from orekit import JArray\n", "\n", "orekit_filename = 'orekit-data'\n", "DM = DataProvidersManager.getInstance()\n", "datafile = File(orekit_filename)\n", "if not datafile.exists():\n", " print('Directory :', datafile.absolutePath, ' not found')\n", "\n", "crawler = DirectoryCrawler(datafile)\n", "DM.clearProviders()\n", "DM.addProvider(crawler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import station data from file" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/yzokras/miniconda3/envs/laserod/lib/python3.7/site-packages/pandas/core/indexing.py:1494: PerformanceWarning: indexing past lexsort depth may impact performance.\n", " return self._getitem_tuple(key)\n" ] } ], "source": [ "stationFile = 'SLRF2014_POS+VEL_2030.0_180504.snx'\n", "stationEccFile = 'ecc_xyz.snx'\n", "import slrDataUtils\n", "stationData = slrDataUtils.parseStationData(stationFile, stationEccFile, startCollectionDate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The orbit determination needs a first guess. For this, we use Two-Line Elements. Retrieving the latest TLE prior to the beginning of the orbit determination. It is important to have a \"fresh\" TLE, because the newer the TLE, the better the orbit estimation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 22195U 92070B 18264.82179554 -.00000009 +00000-0 +00000-0 0 9999\n", "2 22195 052.6446 252.2781 0138214 032.3207 112.2760 06.47294175612723\n" ] } ], "source": [ "rawTle = st.tle(norad_cat_id=sat_list[sc_name]['norad_id'], epoch='<{}'.format(odDate), orderby='epoch desc', limit=1, format='tle')\n", "tleLine1 = rawTle.split('\\n')[0]\n", "tleLine2 = rawTle.split('\\n')[1]\n", "print(tleLine1)\n", "print(tleLine2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up Orekit frames and models" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "from org.orekit.utils import Constants as orekit_constants\n", "\n", "from org.orekit.frames import FramesFactory, ITRFVersion\n", "from org.orekit.utils import IERSConventions\n", "tod = FramesFactory.getTOD(IERSConventions.IERS_2010, False) # Taking tidal effects into account when interpolating EOP parameters\n", "gcrf = FramesFactory.getGCRF()\n", "itrf = FramesFactory.getITRF(IERSConventions.IERS_2010, False)\n", "#itrf = FramesFactory.getITRF(ITRFVersion.ITRF_2014, IERSConventions.IERS_2010, False)\n", "# Selecting frames to use for OD\n", "eci = gcrf\n", "ecef = itrf\n", "\n", "from org.orekit.models.earth import ReferenceEllipsoid\n", "wgs84Ellipsoid = ReferenceEllipsoid.getWgs84(ecef)\n", "from org.orekit.bodies import CelestialBodyFactory\n", "moon = CelestialBodyFactory.getMoon()\n", "sun = CelestialBodyFactory.getSun()\n", "\n", "from org.orekit.time import AbsoluteDate, TimeScalesFactory\n", "utc = TimeScalesFactory.getUTC()\n", "mjd_utc_epoch = AbsoluteDate(1858, 11, 17, 0, 0, 0.0, utc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up the propagator from the initial TLEs" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from org.orekit.propagation.analytical.tle import TLE\n", "orekitTle = TLE(tleLine1, tleLine2)\n", "\n", "from org.orekit.attitudes import NadirPointing\n", "nadirPointing = NadirPointing(eci, wgs84Ellipsoid)\n", "\n", "from org.orekit.propagation.analytical.tle import SGP4\n", "sgp4Propagator = SGP4(orekitTle, nadirPointing, sat_list[sc_name]['mass'])\n", "\n", "tleInitialState = sgp4Propagator.getInitialState()\n", "tleEpoch = tleInitialState.getDate()\n", "tleOrbit_TEME = tleInitialState.getOrbit()\n", "tlePV_ECI = tleOrbit_TEME.getPVCoordinates(eci)\n", "\n", "from org.orekit.orbits import CartesianOrbit\n", "tleOrbit_ECI = CartesianOrbit(tlePV_ECI, eci, wgs84Ellipsoid.getGM())\n", "\n", "from org.orekit.propagation.conversion import DormandPrince853IntegratorBuilder\n", "integratorBuilder = DormandPrince853IntegratorBuilder(prop_min_step, prop_max_step, prop_position_error)\n", "\n", "from org.orekit.propagation.conversion import NumericalPropagatorBuilder\n", "from org.orekit.orbits import PositionAngle\n", "propagatorBuilder = NumericalPropagatorBuilder(tleOrbit_ECI,\n", " integratorBuilder, PositionAngle.MEAN, estimator_position_scale)\n", "propagatorBuilder.setMass(sat_list[sc_name]['mass'])\n", "propagatorBuilder.setAttitudeProvider(nadirPointing)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding perturbation forces to the propagator" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Earth gravity field with degree 64 and order 64\n", "from org.orekit.forces.gravity.potential import GravityFieldFactory\n", "gravityProvider = GravityFieldFactory.getConstantNormalizedProvider(64, 64)\n", "from org.orekit.forces.gravity import HolmesFeatherstoneAttractionModel\n", "gravityAttractionModel = HolmesFeatherstoneAttractionModel(ecef, gravityProvider)\n", "propagatorBuilder.addForceModel(gravityAttractionModel)\n", "\n", "# Moon and Sun perturbations\n", "from org.orekit.forces.gravity import ThirdBodyAttraction\n", "moon_3dbodyattraction = ThirdBodyAttraction(moon)\n", "propagatorBuilder.addForceModel(moon_3dbodyattraction)\n", "sun_3dbodyattraction = ThirdBodyAttraction(sun)\n", "propagatorBuilder.addForceModel(sun_3dbodyattraction)\n", "\n", "# Solar radiation pressure\n", "from org.orekit.forces.radiation import IsotropicRadiationSingleCoefficient\n", "isotropicRadiationSingleCoeff = IsotropicRadiationSingleCoefficient(sat_list[sc_name]['cross_section'], sat_list[sc_name]['cr']);\n", "from org.orekit.forces.radiation import SolarRadiationPressure\n", "solarRadiationPressure = SolarRadiationPressure(sun, wgs84Ellipsoid.getEquatorialRadius(),\n", " isotropicRadiationSingleCoeff)\n", "propagatorBuilder.addForceModel(solarRadiationPressure)\n", "\n", "# Atmospheric drag\n", "from org.orekit.forces.drag.atmosphere.data import MarshallSolarActivityFutureEstimation\n", "msafe = MarshallSolarActivityFutureEstimation(\n", " '(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\\p{Digit}\\p{Digit}\\p{Digit}\\p{Digit}F10\\.(?:txt|TXT)',\n", " MarshallSolarActivityFutureEstimation.StrengthLevel.AVERAGE)\n", "DM.feed(msafe.getSupportedNames(), msafe) # Feeding the F10.7 bulletins to Orekit's data manager\n", "\n", "from org.orekit.forces.drag.atmosphere import NRLMSISE00\n", "atmosphere = NRLMSISE00(msafe, sun, wgs84Ellipsoid)\n", "#from org.orekit.forces.drag.atmosphere import DTM2000\n", "#atmosphere = DTM2000(msafe, sun, wgs84Ellipsoid)\n", "from org.orekit.forces.drag import IsotropicDrag\n", "isotropicDrag = IsotropicDrag(sat_list[sc_name]['cross_section'], sat_list[sc_name]['cd'])\n", "from org.orekit.forces.drag import DragForce\n", "dragForce = DragForce(atmosphere, isotropicDrag)\n", "propagatorBuilder.addForceModel(dragForce)\n", "\n", "# Relativity\n", "#from org.orekit.forces.gravity import Relativity\n", "#relativity = Relativity(orekit_constants.EIGEN5C_EARTH_MU)\n", "#propagatorBuilder.addForceModel(relativity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up the estimator" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from org.hipparchus.linear import QRDecomposer\n", "matrixDecomposer = QRDecomposer(1e-11)\n", "from org.hipparchus.optim.nonlinear.vector.leastsquares import GaussNewtonOptimizer\n", "optimizer = GaussNewtonOptimizer(matrixDecomposer, False)\n", "\n", "from org.orekit.estimation.leastsquares import BatchLSEstimator\n", "estimator = BatchLSEstimator(optimizer, propagatorBuilder)\n", "estimator.setParametersConvergenceThreshold(estimator_convergence_thres)\n", "estimator.setMaxIterations(estimator_max_iterations)\n", "estimator.setMaxEvaluations(estimator_max_evaluations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetching range data\n", "Looking for laser ranging data prior to the OD date.\n", "\n", "The API only allows to look for data using the date formats 2018-07-1%, 2018-07-14% or 2018-07-14 0% for example. As a consequence, the search must be split into several days. The results are then sorted, and the observations which are outside of the date range are deleted." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import slrDataUtils\n", "nptDatasetList = slrDataUtils.querySlrData(username_edc, password_edc, url, 'NPT',\n", " sat_list[sc_name]['cospar_id'], startCollectionDate, odDate)\n", "frdDatasetList = slrDataUtils.querySlrData(username_edc, password_edc, url, 'FRD',\n", " sat_list[sc_name]['cospar_id'], startCollectionDate, odDate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Downloading the list of observations." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from orekit.pyhelpers import *\n", "from org.orekit.estimation.measurements import Range\n", "import slrDataUtils\n", "nptDataFrame = slrDataUtils.dlAndParseSlrData(username_edc, password_edc, url, 'NPT', nptDatasetList)\n", "# Comment out to download Full-Rate data\n", "# frdDataFrame = slrDataUtils.dlAndParseSlrData(username_edc, password_edc, url, 'FRD', frdDatasetList)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding the measurements to the estimator" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "slrDataFrame = nptDataFrame\n", "# Comment out to use Full-Rate data\n", "# slrDataFrame = frdDataFrame\n", "for receiveTime, slrData in slrDataFrame.iterrows():\n", " orekitRange = Range(stationData.loc[slrData['station-id'], 'OrekitGroundStation'], \n", " datetime_to_absolutedate(receiveTime),\n", " slrData['range'],\n", " range_sigma,\n", " range_weight\n", " ) # Uses date of signal reception; https://www.orekit.org/static/apidocs/org/orekit/estimation/measurements/Range.html\n", " estimator.addMeasurement(orekitRange)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performing the OD\n", "Estimate the orbit. This step can take a long time." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "estimatedPropagatorArray = estimator.estimate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the estimated propagator and orbit." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "estimatedPropagator = estimatedPropagatorArray[0]\n", "estimatedInitialState = estimatedPropagator.getInitialState()\n", "actualOdDate = estimatedInitialState.getDate()\n", "estimatedOrbit_init = estimatedInitialState.getOrbit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Covariance analysis\n", "Creating the LVLH frame, computing the covariance matrix in both TOD and LVLH frames" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Creating the LVLH frame \n", "from org.orekit.frames import LocalOrbitalFrame\n", "from org.orekit.frames import LOFType\n", "lvlh = LocalOrbitalFrame(eci, LOFType.LVLH, estimatedPropagator, 'LVLH')\n", "\n", "# Getting covariance matrix in ECI frame\n", "covMat_eci_java = estimator.getPhysicalCovariances(1.0e-10)\n", "\n", "# Converting matrix to LVLH frame\n", "# Getting an inertial frame aligned with the LVLH frame at this instant\n", "# The LVLH is normally not inertial, but this should not affect results too much\n", "# Reference: David Vallado, Covariance Transformations for Satellite Flight Dynamics Operations, 2003\n", "eci2lvlh_frozen = eci.getTransformTo(lvlh, actualOdDate).freeze() \n", "\n", "# Computing Jacobian\n", "from org.orekit.utils import CartesianDerivativesFilter\n", "from orekit.pyhelpers import JArray_double2D\n", "jacobianDoubleArray = JArray_double2D(6, 6)\n", "eci2lvlh_frozen.getJacobian(CartesianDerivativesFilter.USE_PV, jacobianDoubleArray)\n", "from org.hipparchus.linear import Array2DRowRealMatrix\n", "jacobian = Array2DRowRealMatrix(jacobianDoubleArray)\n", "# Applying Jacobian to convert matrix to lvlh\n", "covMat_lvlh_java = jacobian.multiply(\n", " covMat_eci_java.multiply(covMat_eci_java.transpose()))\n", "\n", "# Converting the Java matrices to numpy\n", "import numpy as np\n", "covarianceMat_eci = np.matrix([covMat_eci_java.getRow(iRow) \n", " for iRow in range(0, covMat_eci_java.getRowDimension())])\n", "covarianceMat_lvlh = np.matrix([covMat_lvlh_java.getRow(iRow) \n", " for iRow in range(0, covMat_lvlh_java.getRowDimension())])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Computing the position and velocity standard deviation " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "pos_std_crossTrack = np.sqrt(max(0.0, covarianceMat_lvlh[0,0]))\n", "pos_std_alongTrack = np.sqrt(max(0.0, covarianceMat_lvlh[1,1]))\n", "pos_std_outOfPlane = np.sqrt(max(0.0, covarianceMat_lvlh[2,2]))\n", "vel_std_crossTrack = np.sqrt(max(0.0, covarianceMat_lvlh[3,3])) # In case the value is negative...\n", "vel_std_alongTrack = np.sqrt(max(0.0, covarianceMat_lvlh[4,4]))\n", "vel_std_outOfPlane = np.sqrt(max(0.0, covarianceMat_lvlh[5,5]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing residuals\n", "Getting the estimated and measured ranges." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "propagatorParameters = estimator.getPropagatorParametersDrivers(True)\n", "measurementsParameters = estimator.getMeasurementsParametersDrivers(True)\n", "\n", "lastEstimations = estimator.getLastEstimations()\n", "valueSet = lastEstimations.values()\n", "estimatedMeasurements = valueSet.toArray()\n", "keySet = lastEstimations.keySet()\n", "realMeasurements = keySet.toArray()\n", "\n", "from org.orekit.estimation.measurements import EstimatedMeasurement\n", "from org.orekit.estimation.measurements import Range\n", "\n", "import pandas as pd\n", "observedRangeSeries = pd.Series()\n", "estimatedRangeSeries = pd.Series()\n", "\n", "for estMeas in estimatedMeasurements:\n", " estMeas = EstimatedMeasurement.cast_(estMeas)\n", " observedValue = estMeas.getObservedValue()\n", " estimatedValue = estMeas.getEstimatedValue()\n", " pyDateTime = absolutedate_to_datetime(estMeas.date)\n", " observedRangeSeries[pyDateTime] = observedValue[0]\n", " estimatedRangeSeries[pyDateTime] = estimatedValue[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up Plotly for offline mode" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import plotly.io as pio\n", "pio.renderers.default = 'jupyterlab+notebook'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting residuals" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": 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", 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