This package is on its way to become an archive of scientific routines for data processing related to Arc Matching, Arc Associating, Initial Orbit Determination(IOD), Cataloging OD, and Precise OD. Currently, the package only implements a small number of functional modules, and subsequent modules will be added and updated one after another.
So far, operations on Arc Matching include:
- Matching of angle-only measurements(RA and DEC) to space objects with TLE file;
- Matching of radar range+angle measurements to space objects with TLE file;
Operations on TLE file include:
- Read and parse a TLE file
- Calculation of the mean orbital elements(only long-term items are considered) at a certain epoch
- Orbital Propagation using SGP4/SDP4
Operations on IOD include:
- Estimate classical orbital elements from radar range+angle measurements using
- Gibbs/Herrick-Gibbs method
- Elliptical Orbit Fitting method
- FG-Series method
- Estimate classical orbital elements from optical angle-only measurements using
- Near-Circular Orbit Hypothesis method
- Gauss method
- Laplace method
- Multiple-points Laplace method
- Double-R method
- Gooding method
- FG-Series method
On Linux, macOS and Windows architectures, the binary wheels can be installed using pip by executing one of the following commands:
pip install orbdtools
pip install orbdtools --upgrade # to upgrade a pre-existing installation
Convert classical orbital elements to state vectors.
>>> from orbdtools import KeprvTrans
>>> import numpy as np
>>> # classical orbital elements in form of [a, e, i, Ω, ω, ν]
>>> coe = np.array([7000,0.01,50,100,30,210]) # semi major axis is in [km], and angles are in [deg]
>>> mu = 398600.4418 # GM for the Reference Earth Model - WGS84, [km^3/s^2]
>>> rv = KeprvTrans.coe2rv(coe,mu)
>>> print(rv)
>>> # [ 4.48364689e+03 -2.79409408e+03 -4.68399786e+03 1.21885031e+00 6.81282168e+00 -2.84038655e+00]
>>> # For non-dimensional/dimensionless orbital elements
>>> coe_nd = np.array([1.0974,0.01,50,100,30,210]) # semi major axis is in non-dimensional length unit [L_nd]. For the Reference Earth Model - WGS84, [L_nd]=6378.137 [km] as the equatorial radius.
>>> mu_nd = 1.5 # GM of the central attraction in non-dimensional unit [mu_nd]. For the Reference Earth Model - WGS84, [mu_nd]=398600.4418 [km^3/s^2].
>>> rv_nd = KeprvTrans.coe2rv(coe_nd,mu_nd)
>>> print(rv_nd)
>>> # [ 0.70290773 -0.43803412 -0.73431704 0.18883985 1.05552933 -0.44006895]Revert to classical orbital elements from state vectors.
>>> from orbdtools import KeprvTrans
>>> import numpy as np
>>> rvs = np.array([[ 4.48e+03, -2.79e+03, -4.68e+03, 1.22e+00,6.81e+00, -2.84e+00],[ 5.48e+03, -3.79e+03, -5.68e+03, 1.52e+00,7.81e+00, -3.84e+00]])
>>> mu = 398600.4418 # GM for the Reference Earth Model - WGS84, [km^3/s^2]
>>> coe = KeprvTrans.rv2coe(rvs,mu)
>>> print(coe)
>>> # [[6.98242989e+03 1.12195221e-02 4.99946032e+01 9.99954947e+01 3.60288078e+01 2.03986947e+02] [3.06604952e+04 7.14457354e-01 5.11123776e+01 1.01894775e+02 2.35493764e+02 9.61451955e-01]]
>>> # For non-dimensional/dimensionless state vectors
>>> # The non-dimensional time unit [T_nd] is defined by sqrt([L_nd]**3/[mu_nd]), and non-dimensional velocity unit [v_nd] is defined by [L_nd]/[T_nd]
>>> rvs_nd = np.array([[ 0.70239946, -0.43743181, -0.73375658, 0.15432556, 0.86144022,-0.35924967],[ 0.85918506, -0.5942174 , -0.89054218, 0.19227447, 0.98793658,-0.48574603]])
>>> mu_nd = 1.5
>>> coe_nd = KeprvTrans.rv2coe(rvs_nd,mu_nd)
>>> print(coe_nd)Calculate transformation matrix between Topocentric NEU(North East Up) and ITRF(International Terrestrial Reference Frame).
Reference materials have slightly different definitions to the topocentric horizon coordinate system, and extra attention should be paid when using them. For example, NEU(North East Up) is described by the python package skyfield and this package as that X points North, Y East, and Z Up. SEZ(South East Zenith) is used by Bate, Roger R., et al. Fundamentals of astrodynamics. Courier Dover Publications, 2020. and Vallado, D. A., and W. D. McClain. "Fundamentals of astrodynamics and applications 4th Edition." (2013).; ENZ(East North Zenith) is used by Curtis, Howard. Orbital Mechanics for Engineering Students: Revised Reprint. Butterworth-Heinemann, 2020.
>>> from orbdtools import FrameTrans
>>> lon,lat = 102.5,25.2 # longitude and latitude in [deg]
>>> #lon,lat = [102.5,102,102.5],[25.2,26,16.5]
>>> matrix_trans = FrameTrans.topo_itrf_mat(lon,lat)
>>> topo2itrf_mat = matrix_trans.topo2itrf_mat
>>> itrf2topo_mat = matrix_trans.itrf2topo_mat
>>> print(matrix_trans)
>>> # <MatrixTrans object: 'TOPO' ⇌ 'ITRF'>Calculate the transformation matrix between GCRF(Geocentric Celestial Reference Frame)/ICRF(International Celestial Reference Frame) and ITRF(International Terrestrial Reference Frame).
Ignoring the subtle effects of epoch offset, GCRF/ICRF is equivalent to the J2000(also EME2000, Earth's Mean Equator and Mean Equinox at 12:00 Terrestrial Time on 1 January 2000) reference frame in this package.
>>> from astropy.time import Time
>>> ta = Time('2022-01-02T03:04:05.678') # UTC
>>> # ta = Time(['2022-01-02T03:04:05.678','2022-02-02T03:04:05.678']) # UTC
>>> from orbdtools import FrameTrans
>>> matrix_trans = FrameTrans.gcrf_itrf_mat(ta)
>>> gcrf2itrf_mat = matrix_trans.gcrf2itrf_mat
>>> itrf2gcrf_mat = matrix_trans.itrf2gcrf_matCalculate the transformation matrix between GCRF(Geocentric Celestial Reference Frame) and the topocentric NEU(North East Up) reference frame.
>>> from orbdtools import FrameTrans
>>> from astropy.time import Time
>>> lon,lat = 102.5,25.2
>>> ta = Time(['2022-01-02T03:04:05.678']) # UTC
>>> #lon,lat = [102.5,102,102.5],[25.2,26,16.5]
>>> #ta = Time(['2022-01-02T03:04:05.678','2022-02-02T03:04:05.678']) # UTC
>>> matrix_trans = FrameTrans.gcrf_topo_mat(lon,lat,ta)
>>> gcrf2topo_mat = matrix_trans.gcrf2topo_mat
>>> topo2gcrf_mat = matrix_trans.topo2gcrf_matCalculate the transformation matrix between GCRF(Geocentric Celestial Reference Frame) and the TEME(True Equator, Mean Equinox) reference frame.
>>> from orbdtools import FrameTrans
>>> from astropy.time import Time
>>> ta = Time('2022-01-02T03:04:05.678') # UTC
>>> #ta = Time(['2022-01-02T03:04:05.678','2022-02-02T03:04:05.678']) # UTC
>>> matrix_trans = FrameTrans.gcrf_teme_mat(ta)
>>> gcrf2teme_mat = matrix_trans.gcrf2teme_mat
>>> teme2gcrf_mat = matrix_trans.teme2gcrf_matCalculate the transformation matrix between the Earth Centred Inertial (ECI) reference frame and the PQW(perifocal) reference frame.
>>> from orbdtools import FrameTrans
>>> inc,raan,argp = 30,40,50 # [i, Ω, ω] in unit of [deg]
>>> #inc,raan,argp = [30,40],[40,50],[50,60] # [i, Ω, ω] in unit of [deg]
>>> matrix_trans = FrameTrans.ECI_PQW_mat(inc,raan,argp)
>>> ECI2PQW_mat = matrix_trans.ECI2PQW_mat
>>> PQW2ECI_mat = matrix_trans.PQW2ECI_matCalculate the transformation matrix between the Earth Centred Inertial (ECI) reference frame and the RSW(x -> Radial, y -> Along-track, z -> Cross-track) reference frame.
>>> from orbdtools import FrameTrans
>>> #inc,raan,argp,nu = 30,40,50,60 # [i, Ω, ω, v] in unit of [deg]
>>> inc,raan,argp,nu = [30,40],[40,50],[50,60],[60,70] # [i, Ω, ω, v] in unit of [deg]
>>> matrix_trans = FrameTrans.ECI_RSW_mat(inc,raan,argp,nu)
>>> ECI2RSW_mat = matrix_trans.ECI2RSW_mat
>>> RSW2ECI_mat = matrix_trans.RSW2ECI_matCalculate the transformation matrix between the Earth Centred Inertial (ECI) reference frame and the NTW(x -> Normal, y -> Tangent, z -> Cross-track) reference frame.
>>> from orbdtools import FrameTrans
>>> ecc,inc,raan,argp,nu = 0.1,30,40,50,60 # [i, Ω, ω, v] in unit of [deg]
>>> #ecc,inc,raan,argp,nu = [0.1,0.2],[30,40],[40,50],[50,60],[60,70] # [i, Ω, ω, v] in unit of [deg]
>>> matrix_trans = FrameTrans.ECI_NTW_mat(ecc,inc,raan,argp,nu)
>>> ECI2NTW_mat = matrix_trans.ECI2NTW_mat
>>> NTW2ECI_mat = matrix_trans.NTW2ECI_matCalculate the transformation matrix between the Earth Centred Inertial (ECI) reference frame and the RADAR(x -> Along-track, y -> Cross-track, z -> Radial) reference frame.
>>> from orbdtools import FrameTrans
>>> inc,raan,argp,nu = 30,40,50,60 # [i, Ω, ω, v] in unit of [deg]
>>> #inc,raan,argp,nu = [30,40],[40,50],[50,60],[60,70] # [i, Ω, ω, v] in unit of [deg]
>>> matrix_trans = FrameTrans.ECI_RADAR_mat(inc,raan,argp,nu)
>>> ECI2RADAR_mat = matrix_trans.ECI2RADAR_mat
>>> RADAR2ECI_mat = matrix_trans.RADAR2ECI_mat Calculate the rotation matrix between the RSW(x -> Radial, y -> Along-track, z -> Cross-track) reference frame and the Body-Fixed reference frame.
The transformation between the Body-Fixed(BF) reference frame and the Device-Fixed(DF) reference frame works the same way, and just replace FrameTrans.RSW_BF_mat with FrameTrans.BF_DF_mat.
>>> from orbdtools import FrameTrans
>>> triad,mode = [20,30,40],'euler' # Classic 'ZXZ' rotation transform from RSW to BF
>>> # triad,mode = [20,30,40],'ypr' # Yaw(x)–Pitch(z)–Roll(y) rotation transform from RSW to BF
>>> # triad,mode = [[0,1,0],[0,0,1],[1,0,0]],'matrix' # Each column of matrix is the base vector of BF in RSW
>>> # triad,mode = [1,2,3,4],'quaternion' # Each row is a (possibly non-unit norm) quaternion in scalar-last (x, y, z, w) format.
>>> matrix_trans = FrameTrans.RSW_BF_mat(triad,mode)
>>> RSW2BF_mat = matrix_trans.RSW2BF_mat
>>> BF2RSW_mat = matrix_trans.BF2RSW_mat
>>>
>>> # For multiple dimension
>>> from orbdtools import FrameTrans
>>> triad,mode = [[20,30,40],[60,60,70]],'euler' # Classic 'ZXZ' rotation transform from RSW to BF
>>> #triad,mode = [[20,30,40],[60,60,70]],'ypr' # Yaw(x)–Pitch(z)–Roll(y) rotation transform from RSW to BF
>>> #triad,mode = [[[0,1,0],[0,0,1],[1,0,0]],[[0,-1,0],[0,0,1],[-1,0,0]]],'matrix' # Each column of matrix is the base vector of BF in RSW
>>> #triad,mode = [[1,2,3,4],[5,6,7,8]],'quaternion' # Each row is a (possibly non-unit norm) quaternion in scalar-last (x, y, z, w) format.
>>> matrix_trans = FrameTrans.RSW_BF_mat(triad,mode)
>>> RSW2BF_mat = matrix_trans.RSW2BF_mat
>>> BF2RSW_mat = matrix_trans.BF2RSW_matCalculate the rotation matrix between the Earth Centred Inertial(ECI) reference frame and the Device-Fixed(DF) reference frame.
>>> from orbdtools import FrameTrans
>>> # For a single payload, a single time
>>> triad_RSWBF,mode_RSWBF = triad,mode = [20,30,40],'euler' # Classic 'ZXZ' rotation transform from RSW to BF
>>> triad_BFDF,mode_BFDF = [[0,1,0],[0,0,1],[1,0,0]],'matrix' # Each column of matrix is the base vector of DF in BF
>>> orb_ele = [1.5,0.1,20,30,40,50]
>>> matrix_trans = FrameTrans.ECI_DF_mat(triad_RSWBF,mode_RSWBF,triad_BFDF,mode_BFDF,orb_ele)
>>>
>>> # For multiple payloads, a single time
>>> triad_BFDF,mode_BFDF = triad,mode = [[[0,1,0],[0,0,1],[1,0,0]],[[0,-1,0],[0,0,1],[-1,0,0]]],'matrix' # two payload devices
>>> matrix_trans = FrameTrans.ECI_DF_mat(triad_RSWBF,mode_RSWBF,triad_BFDF,mode_BFDF,orb_ele)
>>>
>>> # For a single payload, multiple time
>>> triad_BFDF,mode_BFDF = triad,mode = [[0,1,0],[0,0,1],[1,0,0]],'matrix'
>>> orb_ele = [[1.5,0.1,20,30,40,50],[1.6,0.2,20,40,50,60]]
>>> #triad_RSWBF,mode_RSWBF = triad,mode = [[20,30,40],[60,60,70]],'euler' # Classic 'ZXZ' rotation transform from RSW to BF
>>> matrix_trans = FrameTrans.ECI_DF_mat(triad_RSWBF,mode_RSWBF,triad_BFDF,mode_BFDF,orb_ele)
>>>
>>> # For multiple payloads, multiple time
>>> triad_BFDF,mode_BFDF = triad,mode = [[[0,1,0],[0,0,1],[1,0,0]],[[0,-1,0],[0,0,1],[-1,0,0]]],'matrix'
>>> orb_ele = [[1.5,0.1,20,30,40,50],[1.6,0.2,20,40,50,60]]
>>> #triad_RSWBF,mode_RSWBF = triad,mode = [[20,30,40],[60,60,70]],'euler' # Classic 'ZXZ' rotation transform from RSW to BF
>>> matrix_trans = FrameTrans.ECI_DF_mat(triad_RSWBF,mode_RSWBF,triad_BFDF,mode_BFDF,orb_ele)
>>> ECI2DF_mat = matrix_trans.ECI2DF_mat
>>> DF2ECI_mat = matrix_trans.DF2ECI_matThe transformation of vectors from one reference frame to another can be calculated using Matrix_dot_Vector in orbdtools.utils.math.
Convert to True Anomaly from Mean anomaly.
>>> from orbdtools import OrbeleTrans
>>> # For elliptic trajectories
>>> Me = 100 # Mean anomaly in [deg]
>>> ecc = 0.1 # Eccentricity
>>> nu = OrbeleTrans.Me_to_nu(Me,ecc)
>>> print(nu)
>>> #For hyperbolic trajectories
>>> Mh = 100 # Mean anomaly in [deg]
>>> ecc = 1.1 # Eccentricity
>>> nu = OrbeleTrans.Mh_to_nu(Mh,ecc)
>>> print(nu)
>>> # For parabolic trajectories
>>> Mp = 100 # Mean anomaly in [deg]
>>> ecc = 1 # Eccentricity
>>> nu = OrbeleTrans.Mp_to_nu(Mp,ecc)
>>> print(nu)
>>> # 110.97777806171158
>>> # 147.3000454161361
>>> # 120.18911304875806Transformation between non-singular orbital elements and classical orbital elements for elliptic orbits
Convert to non-singular orbital elements from classical orbital elements.
The non-singular orbital elements exhibit no singularity of ω for near-circular orbit. It is also known as the first kind of non-singular orbital elements.
>>> a,ecc,inc,raan,argp,nu = 1.2,0.1,50,60,70,80
>>> a,inc,raan,xi,eta,l = OrbeleTrans.coe2nse(a,ecc,inc,raan,argp,nu)
>>> print(a,inc,raan,xi,eta,l)
>>> # 1.2 50 60 0.03420201433256689 0.09396926207859084 138.87814991243528Revert to classical orbital elements from non-singular orbital elements.
>>> a,inc,raan,xi,eta,l = 1.2,50,60,0.0342,0.0940,138.8781
>>> a,ecc,inc,raan,argp,nu = OrbeleTrans.nse2coe(a,inc,raan,xi,eta,l)
>>> print(a,ecc,inc,raan,argp,nu)
>>> # 1.2 0.1000281960249209 50 60 70.00710602013012 79.99572274415195Convert to modified equinoctial orbital elements from classical orbital elements.
The modified equinoctial orbital elements exhibit no singularity of both ω and Ω for near-circular orbit with orbital plane close to equator. It is also known as the second kind of non-singular orbital elements.
>>> a,ecc,inc,raan,argp,nu = 1.2,0.1,0.01,60,70,80
>>> p,f,g,h,k,L = OrbeleTrans.coe2mee(a,ecc,inc,raan,argp,nu)
>>> print(p,f,g,h,k,L)
>>> # 1.188 -0.06427876096865393 0.07660444431189782 4.363323141062026e-05 7.557497370160451e-05 198.87814991243528Revert to classical orbital elements from modified equinoctial orbital elements.
>>> p,f,g,h,k,L = 1.188,-0.0643,0.0766,4.3633e-05,7.5575e-05,198.8781
>>> a,ecc,inc,raan,argp,nu = OrbeleTrans.mee2coe(p,f,g,h,k,L)
>>> print(a,ecc,inc,raan,argp,nu)
>>> # 1.2000024848536301 0.10001024947474133 0.00999998935100991 60.00014021318978 70.0108175075162 79.98961193079703Transformation between mean orbital elements and osculating orbital elements associated to SGP4/SDP4 propagator
Convert mean orbital elements to osculating orbital elements.
>>> from orbdtools import OrbeleTrans
>>> from astropy.time import Time
>>> mean_ele = [7000,0.01,50,100,30,210] # in form of [a, e, i, Ω, ω, v] in TEME
>>> epoch = Time('2022-06-07T08:09:12.345')
>>> oscu_ele = OrbeleTrans.mean2osculating(mean_ele,epoch)
>>> print(oscu_ele)
>>> # [6.99736629e+03 1.06734477e-02 4.99907889e+01 1.00021727e+02 3.31317206e+01 2.06884978e+02]Revert to mean orbital elements from osculating orbital elements.
>>> from astropy.time import Time
>>> oscu_ele = [6.9974e+03,1.0673e-02,4.9991e+01,1.0002e+02,3.3132e+01,2.0688e+02] # in form of [a, e, i, Ω, ω, v] in TEME
>>> epoch = Time('2022-06-07T08:09:12.345')
>>> mean_ele = OrbeleTrans.osculating2mean(oscu_ele,epoch)
>>> print(mean_ele)
>>> # [7.00003366e+03 9.99954249e-03 5.00002077e+01 9.99982732e+01 3.00004171e+01 2.09994881e+02]Transform classical orbital elements between two reference frames, especially between 'TEME' and 'GCRF'. What needs extra attention is that the reference frames on transformation must be defined as hand-right.
>>> from orbdtools import OrbeleTrans
>>> from orbdtools import FrameTrans
>>> coe_from = [6.9974e+03,1.0673e-02,4.9991e+01,1.0002e+02,3.3132e+01,2.0688e+02] # in TEME
>>> trans_matrix = FrameTrans.gcrf_teme_mat(epoch)
>>> teme2gcrf_mat = trans_matrix.teme2gcrf_mat # transformation matrix from TEME to GCRF
>>> coe_to = OrbeleTrans.coe_trans(teme2gcrf_mat,coe_from)
>>> print(coe_to)
>>> # [6.99740000e+03 1.06730000e-02 5.01127908e+01 9.97161144e+01 3.31576626e+01 2.06880000e+02]Transform state vector between two reference frames.
>>> from orbdtools import OrbeleTrans
>>> rv_from = [4.4836e+03,-2.7941e+03,-4.6840e+03,1.2189,6.8128,-2.8404] # in unit of [km,km/s] in TEME
>>> rv_to = OrbeleTrans.rv_trans(teme2gcrf_mat,rv_from)
>>> print(rv_to)
>>> # [ 4.45943241e+03 -2.81665205e+03 -4.69355448e+03 1.24694023e+00 6.80654150e+00 -2.84323163e+00]Download TLE files from SPACETRACK
>>> from orbdtools import TLE
>>> tle_file = TLE.download([52132,51454,37637,26758,44691])
>>> # tle_file = TLE.download('satno.txt')A sample of the satno.txt file is as follows:
#satno
12345
23469
45678
>>> import numpy as np
>>> tle_file = 'test/test1/tle_20220524.txt'
>>> epoch_obs = '2022-05-24T08:38:34.000Z' # Approximate epoch of the observed arc, which is optional
>>> tle = TLE.from_file(tle_file,epoch_obs) # Only TLEs within a week before and after the observation epoch are considered
>>> # tle = TLE.from_file(tle_file) # All TLEs are considered
>>> print(tle.range_epoch) # Release epoch coverage for TLE files, [min, max]
>>> # ['2022-05-18T23:07:15.444', '2022-05-24T06:32:39.874']
>>> print(tle.df)
>>> print(tle._statistic)
Retrieve the TLE database with the space objects of interest and generate a simplified database.
>>> sats_id = [10,47,58,52139]
>>> tle_r = tle.retrieve(sats_id)>>> tle_epoch = tle.atEpoch(epoch_obs)
>>> print(tle_epoch.df)
>>> # tle_epoch = tle.atEpoch(epoch_obs,sats_id)
Calculate the cartesian coordinates of space objects in GCRF(Geocentric Celetial Reference Frame) over a period of time
>>> t_list = ['2022-05-23T16:22:49.408Z', '2022-05-23T18:48:34.488Z',
'2022-05-23T18:09:35.640Z', '2022-05-23T20:54:20.228Z',
'2022-05-23T20:29:03.621Z', '2022-05-23T23:24:11.831Z',
'2022-05-24T00:38:08.803Z', '2022-05-24T02:33:32.466Z',
'2022-05-23T21:10:37.703Z', '2022-05-23T17:48:24.865Z']
>>> xyz_gcrf = tle.predict(t_list)
>>> # sats_id = [47,58,52139,52140,52150] # Norad IDs of space objects
>>> # xyz_gcrf = tle.predict(t_list,sats_id)
>>> print(xyz_gcrf.shape)
>>> # (4904, 10, 3)Load the observation file and extract the optical angle-only measurement data.
>>> import numpy as np
>>> obs_data = np.loadtxt('test/test1/optical_obs.dat',dtype=str,skiprows=1) # Load the observation file
>>> # extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> xyz_site = obs_data[:,1:4].astype(float) # Cartesian coordinates of the site in GCRF, [km]
>>> radec = obs_data[:,4:6].astype(float) # Ra and Dec of space object, [deg]Match the arc to space objects with TLE file.
>>> from orbdtools import ArcObs
>>> arc_optical = ArcObs({'t':t,'radec':radec,'xyz_site':xyz_site}) # Load the necessary data
>>>
>>> # Use the LOWESS(LOcally Weighted Scatterplot Smoothing) method to remove outliers, optional
>>> arc_optical.lowess_smooth()
>>> arc_optical.arc_match(tle) # Match the observation arc to TLE
>>>
>>> print(arc_optical.code_match)
>>> print(arc_optical.satid)
>>> print(arc_optical.disp_match)1
1616
Target ID: 1616
Three types of matching results are summaried as follows
| Match Code | Object ID | Solution Case | Status | What to Do Next |
|---|---|---|---|---|
| 1 | NORAD ID | Unique solution | Success | |
| 0 | None | No solution | Failure | increase threshold |
| -1 | list of NORAD IDs | Multiple solutions | Failure | decrease threshold |
Load the observation file and extract the space-based radar measurement data.
>>> import numpy as np
>>>
>>> obs_data = np.loadtxt('test/test1/radar_obs.dat',dtype=str,skiprows=1) # Load the observation file
>>> # extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> orbele_site = obs_data[:,1:7].astype(float) # Orbital elements(a,ecc,inc,raan,argp,true_anomaly) of the site
>>> xyz_site = obs_data[:,7:10].astype(float) # Cartesian coordinates of the site in GCRF, [km]
>>> azalt = obs_data[:,10:12].astype(float) # Azimuth and Altitude angle of space object, [deg]
>>> r = obs_data[:,12].astype(float) # Slant distance of the space object relative to the site, [km]Match the arc to space objects with TLE file. Note: the RADAR reference frame is defined as right-handed with x:Along-track,y:Cross-track,z:Radial; azimuth is measured clockwise from the x-axis.
>>> from orbdtools import ArcObs
>>> arc_radar = ArcObs({'t':t,'azalt':azalt,'r':r,'xyz_site':xyz_site,'orbele_site':orbele_site}) # Load the necessary data
>>> arc_radar.lowess_smooth()
>>> arc_radar.arc_match(tle)
>>>
>>> print(arc_radar.code_match)
>>> print(arc_radar.satid)
>>> print(arc_radar.disp_match)1
1616
Target ID: 1616
For optical angle-only measurement data, the methods of Near-Circular Orbit Hypothesis, Gauss, Laplace, Multiple-points Laplace, Double-R, Gooding, and FG-Series are provided for determining the initial orbit of space objects in this package. For radar range+angle measurement data, the methods of Gibbs/Herrick-Gibbs, Elliptical Orbit Fitting and FG-Series are provided for determining the initial orbit of space objects in this package.
Here we use ground-based optical angle-only measurements, space-based optical angle-only measurements and radar range+angle measurements as examples to test various IOD methods and compare them with the true orbits from TLE.
Extract the necessary information for IOD from ground-based optical angle-only measurements.
>>> import numpy as np
>>> obs_data = np.loadtxt('test/test5/T25872_KUN2_2.dat',dtype=str,skiprows=1) # Load the observation file
>>> obs_data = obs_data[::10]
>>> # Extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> radec = obs_data[:,1:3].astype(float) # Ra and Dec of the space object, in [hour,deg]
>>> xyz_site = obs_data[:,3:6].astype(float) # Cartesian coordinates of the site in GCRF, in [km]
>>> radec[:,0] *= 15 # Convert hour angle to degreesLoad the extracted data to ArcObs, and eliminate outliers using the method of LOWESS.
>>> from orbdtools import ArcObs
>>> arc_optical = ArcObs({'t':t,'radec':radec,'xyz_site':xyz_site})
>>> arc_optical.lowess_smooth() # Eliminate outliers using the method of LOWESSSet the Earth as the central body of attraction.
>>> from orbdtools import Body
>>> earth = Body.from_name('Earth')
>>> arc_iod = arc_optical.iod(earth)The traditional two-point Near-Circular Orbit Hypothesis method is improved by combining the three-point Gibbs/Herrick-Gibbs method in this package. For more information about the traditional method, please refer to
- 张晓祥,吴连大,熊建宁.空间目标的圆轨道跟踪法[J].天文学报, 2003, 44(4):11.DOI:10.3321/j.issn:0001-5245.2003.04.010.
>>> arc_iod.circular(ellipse_only=False)
>>> print(arc_iod)
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.310295 0.000002 52.01579 51.568628 314.478385 180.0 180.0 1.144681 successThe optional parameter ellipse_only is a switch for filtering out elliptical orbits with semi-major axis greater than the equatorial radius of the central body and O-C less than the preset threshold. If True, only elliptical orbits are considered as valid, otherwise all orbits including parabolic and hyperbolic orbits are considered as valid.
For more information about the method, please refer to
- Curtis H D. Orbital Mechanics for Engineering Students: Revised 4th edition[M]. Butterworth-Heinemann, 2020.
>>> arc_iod.gauss(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.313445 0.00228 52.038608 51.553549 148.236551 346.253751 346.315736 1.146053 successFor more information about the method, please refer to
-
Vallado D. Fundamentals of Astrodynamics and Applications(4th)[M], Microcosm Press, 2013.
-
Bate R R, Mueller D D, White J E, et al. Fundamentals of astrodynamics(2nd)[M]. Courier Dover Publications, 2020.
>>> arc_iod.laplace(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.135273 0.224665 52.795486 54.588879 325.396385 168.002859 161.612157 1.038254 failedFor more information about the method, please refer to
- Bate R R, Mueller D D, White J E, et al. Fundamentals of astrodynamics(2nd)[M]. Courier Dover Publications, 2020.
>>> arc_iod.multilaplace(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.275725 0.072221 53.501942 53.019178 325.045457 168.931844 167.254415 1.126531 failedThe traditional three-point Double-R method is improved by combining the three-point Gibbs/Herrick-Gibbs method in this package. For more information about the traditional method, please refer to
- Vallado D. Fundamentals of Astrodynamics and Applications(4th)[M], Microcosm Press, 2013.
>>> arc_iod.doubleR(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.313601 0.002381 52.039102 51.554024 147.573208 346.917006 346.978664 1.146121 successFor more information about the method, please refer to
- Gooding R H. A new procedure for the solution of the classical problem of minimal orbit determination from three lines of sight[J]. Celestial Mechanics and Dynamical Astronomy, 1996, 66: 387-423.
- Izzo D. Revisiting Lambert’s problem[J]. Celestial Mechanics and Dynamical Astronomy, 2015, 121: 1-15.
- Curtis H D. Orbital Mechanics for Engineering Students: Revised 4th edition[M]. Butterworth-Heinemann, 2020.
>>> # solve the Lambert's problem with the universal variable method
>>> ele_dict = arc_iod.gooding(method='universal',ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.310295 0.000003 52.009667 51.56095 314.498906 180.0 180.0 1.144681 success
>>> # solve the Lambert's problem with the method by Izzo
>>> arc_iod.gooding(method='izzo',ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.310295 0.000003 52.009667 51.56095 314.498906 180.0 180.0 1.144681 successFor more information about the method, please refer to
-
李光宇.天体测量和天体力学基础[M].科学出版社,2015.
-
刘林.卫星轨道力学算法[M].南京大学出版社,2019.
-
Bate R R, Mueller D D, White J E, et al. Fundamentals of astrodynamics(2nd)[M]. Courier Dover Publications, 2020.
>>> arc_iod.fg_series(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-01-18T21:31:36.000 1.31294 0.00191 52.036942 51.554157 149.873847 344.616532 344.674512 1.145834 successCompare the results with the true orbits from TLE.
>>> from orbdtools import TLE
>>> tle_file = 'test/test5/tle_20220119.txt'
>>> tle = TLE.from_file(tle_file)
>>> tle_update = tle.atEpoch('2022-01-18T21:31:36.000')
>>> tle_df = tle_update.df
>>> sat_df = tle_df[tle_df['noradid']==25872]
>>> print(sat_df.to_string())
>>> # noradid a ecc inc raan argp M h bstar epoch
>>> # 1900 25872 1.312288 0.001365 51.9388 51.796242 187.68949 306.956591 1.14555 0.000343 2022-01-18T21:31:36.000ZNote that the Mean Elements involved in the SGP4 propagator are in a True Equator Mean Equinox (TEME) reference frame. The Orbital Elements from IOD are in an ECI reference frame.
For the space-based optical angle-only measurements, we use the same processing flow as the ground-based measurements to test IOD methods mentioned above.
>>> import numpy as np
>>> obs_data = np.loadtxt('test/test3/T22694_S1_1_C5_1.dat',dtype=str,skiprows=1) # Load the observation file
>>> obs_data = obs_data[::5]
>>> # extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> radec = obs_data[:,1:3].astype(float) # Ra and Dec of space object, in [deg]
>>> radec[:,0] *= 15 # Convert hours to degrees
>>> xyz_site = obs_data[:,3:6].astype(float) # Cartesian coordinates of the site in GCRF, [km]
>>> from orbdtools import ArcObs
>>> arc_optical = ArcObs({'t':t,'radec':radec,'xyz_site':xyz_site}) # Load the necessary data
>>> arc_optical.lowess_smooth()
>>> arc_iod.circular(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> arc_iod.gauss(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> arc_iod.laplace(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> arc_iod.multilaplace(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> arc_iod.doubleR(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> ele_dict = arc_iod.gooding(method='universal',ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> arc_iod.gooding(method='izzo',ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> arc_iod.fg_series(ellipse_only=False)
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 6.620564 0.000002 15.122863 6.748521 184.996759 1.038925 1.03892 2.573046 success
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 -0.002950 77687.676046 45.163396 286.360923 179.950664 8.310002 333.175406 4219.232928 failed
>>> # 1 2022-03-24T19:43:11.000 6.859966 0.025903 18.530465 3.513682 170.273591 15.725365 14.935701 2.618275 success
>>> # 2 2022-03-24T19:43:11.000 6.616976 0.007160 2.819296 204.772918 181.406380 184.848294 184.918012 2.572282 failed
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 -0.002950 77704.757170 45.163444 286.360610 179.949294 8.311361 223.906534 4220.111239 failed
>>> # 1 2022-03-24T19:43:11.000 6.859363 0.025809 18.531228 3.511614 170.260444 15.738826 14.951304 2.618166 success
>>> # 2 2022-03-24T19:43:11.000 6.616654 0.007214 2.820695 204.774097 181.496819 184.758445 184.827393 2.572219 failed
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 -0.029135 2520.867018 44.311504 279.565977 0.649530 7.579402 123.613228 430.287289 failed
>>> # 1 2022-03-24T19:43:11.000 6.548644 0.008583 0.345056 21.958307 1.134312 184.607814 184.687336 2.558938 success
>>> # 2 2022-03-24T19:43:11.000 6.422555 0.022935 10.780522 11.045074 356.693901 189.357780 189.792484 2.533609 success
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 6.455367 0.025672 15.406771 6.858407 10.275297 175.652244 175.424872 2.539904 success
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 6.620564 0.000002 15.122845 6.749191 186.035035 0.000815 0.000815 2.573046 success
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 6.620564 0.000002 15.122845 6.749191 186.035035 0.000815 0.000815 2.573046 success
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-03-24T19:43:11.000 6.676907 0.008458 15.048129 6.719165 189.801615 356.263119 356.325893 2.583879 success
>>> from orbdtools import TLE
>>> tle_file = 'test/test3/tle_20220325.txt'
>>> tle = TLE.from_file(tle_file)
>>> tle_update = tle.atEpoch('2022-03-24T19:43:11.000')
>>> tle_df = tle_update.df
>>> sat_df = tle_df[tle_df['noradid']==22694]
>>> print(sat_df.to_string())
>>> # noradid a ecc inc raan argp M h bstar epoch
>>> # 203 22694 6.611253 0.000843 14.627818 7.031644 295.619471 250.970436 2.571235 0.0 2022-03-24T19:43:11.000ZFor the space-based radar range+angle measurements, we use the methods of Gibbs/Herrick-Gibbs and Elliptical Orbit Fitting to determine the initial orbit of space objects.
Extract the necessary information for Initial Orbit Determination(IOD) from space-based radar range+angle measurements.
>>> import numpy as np
>>> obs_data = np.loadtxt('test/test1/radar_obs.dat',dtype=str,skiprows=1) # Load the observation file
>>> # extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> orbele_site = obs_data[:,1:7].astype(float) # Orbital elements(a,ecc,inc,raan,argp,true_anomaly) of the site
>>> xyz_site = obs_data[:,7:10].astype(float) # Cartesian coordinates of the site in GCRF, [km]
>>> azalt = obs_data[:,10:12].astype(float) # Azimuth and Altitude angle of space object, [deg]
>>> r = obs_data[:,12].astype(float) # Slant distance of the space object relative to the site, [km]Load the extracted data to ArcObs, and eliminate outliers using the method of LOWESS.
>>> from orbdtools import ArcObs
>>> arc_radar = ArcObs({'t':t,'azalt':azalt,'r':r,'xyz_site':xyz_site,'orbele_site':orbele_site}) # Load the necessary data
>>> arc_radar.lowess_smooth()Set the Earth as the central body of attraction.
>>> from orbdtools import Body
>>> earth = Body.from_name('Earth')
>>> arc_iod = arc_radar.iod(earth)For more information about the method, please refer to
-
Vallado D. Fundamentals of Astrodynamics and Applications(4th)[M], Microcosm Press, 2013.
-
Curtis H D. Orbital Mechanics for Engineering Students: Revised 4th edition[M]. Butterworth-Heinemann, 2020.
>>> arc_iod.gibbs()
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-05-24T08:38:34.000 1.191607 0.106731 144.132624 292.686583 269.560174 179.172878 178.981084 1.085372 success>>> arc_iod.ellipse()
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-05-24T08:38:34.000 1.191645 0.106694 144.1329 292.686922 269.482702 179.250624 179.076922 1.085394 success>>> arc_iod.fg_series()
>>> print(arc_iod.df.to_string())
>>> # epoch a ecc inc raan argp nu M h status
>>> # 0 2022-05-24T08:38:34.500 1.191482 0.106847 144.132644 292.666661 269.549063 179.190142 179.002129 1.085302 successCompare the results with the true orbits from TLE.
>>> from orbdtools import TLE
>>> tle_file = 'test/test1/tle_20220524.txt'
>>> tle = TLE.from_file(tle_file)
>>> tle_update = tle.atEpoch('2022-05-24T08:38:34.000')
>>> tle_df = tle_update.df
>>> sat_df = tle_df[tle_df['noradid']==1616]
>>> print(sat_df.to_string())
>>> # noradid a ecc inc raan argp M h bstar epoch
>>> # 46 1616 1.191409 0.10819 144.2312 293.055123 269.557158 179.098994 1.08511 0.000606 2022-05-24T08:38:34.000ZThe SGP4 propagator is used to implement the Cataloging OD.
Read, load, and preprocess the first segment of observation data.
>>> import numpy as np
>>> obs_data = np.loadtxt('test/test6/T38044_S1_1_C1_1.dat',dtype=str,skiprows=1) # Load the observation file
>>> # extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> xyz_site = obs_data[:,1:4].astype(float) # Cartesian coordinates of the site in GCRF, [km]
>>> radec = obs_data[:,4:6].astype(float) # Ra and Dec of space object, in [deg]
>>> radec[:,0] *= 15 # Convert hours to degrees
>>> from orbdtools import ArcObs
>>> arc_optical1 = ArcObs({'t':t,'radec':radec,'xyz_site':xyz_site}) # Load the necessary data
>>> arc_optical1.lowess_smooth()
>>>print(arc_optical1)
>>> # <ArcObs object: OPTICAL Start Time = 2022-07-21T17:39:23.000Z TOF ≈ 14s N_DATA = 15 N_OUTLIER = 0>Read, load, and preprocess the second segment of observation data.
>>> import numpy as np
>>> obs_data = np.loadtxt('test/test6/T38044_S1_2_C1_2.dat',dtype=str,skiprows=1) # Load the observation file
>>> # extract the necessary data
>>> t = obs_data[:,0] # Obsevation time in UTC
>>> xyz_site = obs_data[:,1:4].astype(float) # Cartesian coordinates of the site in GCRF, [km]
>>> radec = obs_data[:,4:6].astype(float) # Ra and Dec of space object, in [deg]
>>> radec[:,0] *= 15 # Convert hours to degrees
>>> from orbdtools import ArcObs
>>> arc_optical2 = ArcObs({'t':t,'radec':radec,'xyz_site':xyz_site}) # Load the necessary data
>>> arc_optical2.lowess_smooth()
>>> print(arc_optical2)
>>> # <ArcObs object: OPTICAL Start Time = 2022-07-22T04:07:14.000Z TOF ≈ 15s N_DATA = 16 N_OUTLIER = 0>Simply join multiple tracklets of same type into one long arc.
>>> arc_optical = arc_optical1.join(arc_optical2)
>>> print(arc_optical)
>>> # <ArcObs object: OPTICAL Start Time = 2022-07-21T17:39:23.000Z TOF ≈ 37686s N_DATA = 31 N_OUTLIER = 0>Read the TLE file and build the TLE database.
>>> from orbdtools import TLE
>>> tle_file = 'test/test6/tle_20220722.txt'
>>> tle = TLE.from_file(tle_file)
>>> print(tle)
>>> # <TLE object: Counts = 5085 Statistic =
>>> # a ecc inc raan argp h bstar epoch
>>> # mean 1.118852 0.003734 69.373955 187.292529 121.009249 1.057371 -0.000805 2022-07-21T06:19:10.865
>>> # std 0.049261 0.015257 19.375287 100.075645 88.498459 0.022713 0.012420 34.776595
>>> # min 1.043196 0.000010 0.048900 0.184800 0.072600 1.021369 -0.324750 2015-10-06T21:31:27.406
>>> # 25% 1.085838 0.000156 53.055000 103.900700 63.156600 1.042035 -0.000077 2022-07-21T16:52:32.458
>>> # 50% 1.085854 0.000219 64.850700 195.074100 83.576300 1.042043 0.000038 2022-07-21T18:54:48.226
>>> # 75% 1.151537 0.001668 86.446100 273.780000 157.690000 1.073042 0.000130 2022-07-21T20:29:56.001
>>> # max 1.312739 0.199768 144.639200 359.945100 359.999900 1.145748 0.110510 2022-07-22T00:52:00.062 >Judging from the statistics of TLE release epochs, some TLEs of space targets have been lost. Next, make a cataloging OD and update the known TLE.
>>> arc_cod = arc_optical.cod_sgp4(tle=tle,satid=38044})
>>> print(arc_cod)
>>> # <COD object: OPTICAL Start Time = 2022-07-21T17:39:23.000Z TOF ≈ 37686s Method = SGP4 ref = TEME Elements = [{'epoch': '2022-07-22T04:07:29.000', 'a': 1.2216952647118227, 'ecc': 0.0001034000939021131, 'inc': 51.9906, 'raan': 350.34109619644204, 'argp': 93.57548154290153, 'nu': 302.5034596834521, 'M': 302.5134520405819, 'h': 1.1053032396812972, 'bstar': 2.2761e-05, 'status': 'success'}]>
>>> print(arc_cod.rms)
>>> # 3.8832929209207407e-10Use the Double-R method to perform IOD on the second segment of observation data, and initial orbital elements are achieved.
>>> # Set the Earth as the central body of attraction
>>> from orbdtools import Body
>>> earth = Body.from_name('Earth')
>>> arc_iod2 = arc_optical2.iod(earth)
>>> arc_iod2.doubleR()
>>> print(arc_iod2)
>>> # <IOD object: OPTICAL Start Time = 2022-07-22T04:07:14.000Z TOF ≈ 15s Central Attraction = <Body object: Earth(♁)> Method = Double-R Elements = [{'epoch': '2022-07-22T04:07:22.000', 'a': 1.2214720783777222, 'ecc': 0.004615520543873671, 'inc': 51.95333793996982, 'raan': 350.14859234189373, 'argp': 311.12684765270643, 'nu': 84.36095628468172, 'M': 83.83479693639099, 'h': 1.1051905072527204, 'status': 'success'}]>
>>> ele0_2 = arc_iod2.df.to_dict('records')[0]
>>> print(ele)
>>> # {'epoch': '2022-07-22T04:07:22.000','a': 1.2214720783777222,'ecc': 0.004615520543873671,'inc': 51.95333793996982,'raan': 350.14859234189373,'argp': 311.12684765270643,'nu': 84.36095628468172,'M': 83.83479693639099,'h': 1.1051905072527204,'status': 'success'}Next, make a cataloging OD and generate a new TLE.
>>> arc_cod = arc_optical.cod_sgp4(ele0_2,satid=38044,**{'intldesg':'11080E'})
>>> print(arc_cod)
>>> # <COD object: OPTICAL Start Time = 2022-07-21T17:39:23.000Z TOF ≈ 37686s Method = SGP4 ref = TEME Elements = [{'epoch': '2022-07-22T04:07:22.000', 'a': 1.2221352337224314, 'ecc': 0.00460718734467556, 'inc': 51.97968991527254, 'raan': 350.3335374798999, 'argp': 311.29131728492814, 'nu': 84.42427214124324, 'M': 83.89900344859379, 'h': 1.105490521201248, 'bstar': 0.0012188082859718142, 'status': 'success'}]>
>>> print(arc_cod.rms)
>>> # 3.9541376373616986e-08
>>> print(arc_cod.tle_str)
>>> # >>> from astropy.time import Time
>>> from orbdtools.cod.sgp4 import sgp4_bstar
>>>
>>> # Mean Elements in TEME at epoch1
>>> epoch1 = Time('2022-05-23T22:00:02.000Z')
>>> ele1_teme = [1.071459,0.000154,53.2175,182.0098,49.6208,205.5672]
>>> # Mean Elements in TEME at epoch2
>>> epoch2 = Time('2022-05-25T08:38:34.000Z')
>>> ele2_teme = [1.072465,0.000159,53.2175,175.255990,58.766942,261.018083]
>>>
>>> # Calculate B*
>>> a_ecc_inc1 = ele1_teme[:3]
>>> a_ecc_inc2 = ele2_teme[:3]
>>> bstar1 = sgp4_bstar.bstar_calculate(a_ecc_inc1,a_ecc_inc2,epoch1,epoch2,degrees=True)
>>>
>>> # Estimate B*
>>> bstar2 = sgp4_bstar.bstar_estimate(ele1_teme,ele2_teme,epoch1,epoch2,ref='TEME')
>>>
>>> print('Calculated B*: ',bstar1)
>>> print('Estimated B*: ',bstar2)
>>>
>>> # Calculated B*: -0.02118445232232004
>>> # Estimated B*: -0.02128338135900665-
0.2.2 — Jan 25, 2024
- Added the conversion between state vector(position and velocity) and modified equinoctial elements.
-
0.2.1 — Dec 04, 2023
- Improved the FG-Series method for angle-only measurements by using the IOD results from the Near-Circular Orbit Hypothesis method as the initial value.
- Fixed the bug that caused module import failure due to duplicate names of sgp4_od.py and sgp4_od directories by deleting redundant sgp4_od.py files.
-
0.2.0 — Oct 18, 2023
- Added the implementation of IOD with the FG-Series method for radar range+angle measurements.
- Added the implementation of Cataloging OD based on the SGP4/SDP4 propagator.
- Added the implementation of fusing multiple tracklets.
- Added the implementation of the calculation and estimation of Bstar involved in SGP4 propagator based on Mean Elements at two epoch times.
- Added statistics for TLE database
- Added an optional argument
sats_idtotle.atEpoch(). It is now possible to propagate the mean orbit to a specific epoch only for the space objects of interest. - Added a new method
retrievetoTLEobject. It is now possible to simplify the TLE database by the space objects of interest.
-
0.1.1 — Sep 23, 2023
- Fixed the bug that when downloading TLE data from SpaceTrack, the automatically generated wrong authentication file due to incorrect user name or password input could no longer be updated.
-
0.1.0 — Sep 13, 2023
- Added transformation between Classical Orbital Elements (COE) and State Vectors.
- Added transformation among Classical Orbital Elements, especially transformation between mean orbital elements and osculating orbital elements associated to SGP4/SDP4 propagator.
- Added computation of matrix associated to transformation among a variety of reference frames.
- Added some classic initial orbit determination methods, including the methods of Near-Circular Orbit Hypothesis, Gauss, Laplace, Multiple-points Laplace, Double-R, Gooding, and FG-Series suitable for optical angle-only data, and the methods of Gibbs/Herrick-Gibbs and Elliptical Orbit Fitting suitable for radar measurement data(range+angle).
-
0.0.3 — Aug 04, 2023
- Change [Ra,Dec] to [Az,Alt] for space-based radar measurement data.
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0.0.2 — Jul 23, 2023
- Adjusted the default matching threshold to improve the matching success rate.
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0.0.1 — Jul 18, 2023
- The orbdtools package was released.
- Implement the Arc Associating between two tracklets.
- Implement the Arc Associating among tens of thousands of tracklets with the help of SQL.
- Implement the Battin algorithm for solving the lambert's problem.
- Implement the IOD for a long arc spanning multiple revolutions.