![]() Their inclination are positive on different times over year. May be zenith angle decreases by time… but why ?! Optical range values have positive inclination. It seems in all optical observations, satellite going to away by time. Of course this is acceptable, but there are something systematic i think. I have range measurements from radiometric observations too.īut in all data sets, radiometric range values differs from range values obtained by optical observations. I obtain (ra, dec) measurements of same satellite from two different sites, synchronized by linear interpolation on time. So, what do you think about difference?īut, i don’t figure out one point. V3b1 = topo2.getStaticTransformTo(inertialFrame, date1).transformVector(v3b) # same for observer1ī = Vector3D.angle(v1, v3a1) * 180 / np.piĬ = Vector3D.angle(v2, v3b1) * 180 / np.pi V3a1 = topo1.getStaticTransformTo(inertialFrame, date1).transformVector(v3a) # observer2’s coordinates as seen by observer1, transformed to inertialFrame V3b = topo1.getPVCoordinates(date1, topo2).position.normalize() # topo2 coordinates of observer1 V3a = topo2.getPVCoordinates(date1, topo1).position.normalize() # topo1 coordinates of observer2 Pos2 = topo2.getPVCoordinates(date1, inertialFrame).positionĭist = pos1.subtract(pos2).norm # linear distance between observer1 and 2 Pos1 = topo1.getPVCoordinates(date1, inertialFrame).position InertialFrame = FramesFactory.getEME2000() Now i think i’m very close to solution, thank you np.pi/180, -5.927083333333334 np.pi/180) # observed from observer1, satellite’s observed ra-dec values Perhaps it’s reason very simple, but i cannot find… Pos2 = topo2.getPVCoordinates(date1, topo1).position # same but for topo2Ī = np.sqrt((pos1.x 2) + (pos1.y2) + (pos1.z**2)) # this is linear distance between B and C observed vectors, vector_fromTo angles are topocentric right ascension and declination values at observer#1 or #2 Pos1 = topo1.getPVCoordinates(date1, topo2).position # topo1 pv coordinates at observation moment from topo2 Topo2 = TopocentricFrame(body, GeodeticPoint(lat2, lon2, alt2), “observer2”)ĭate1 = AbsoluteDate(2023, 1, 3, 7, 0, 0.0, TimeScalesFactory.getUTC()) Topo1 = TopocentricFrame(body, GeodeticPoint(lat1, lon1, alt1), “observer1”) Itrf = FramesFactory.getITRF(IERSConventions.IERS_2010, True)īody = OneAxisEllipsoid(Constants.WGS84_EARTH_EQUATORIAL_RADIUS, Constants.WGS84_EARTH_FLATTENING, itrf) Hereafter i’ll use numbers for to explain myself more clearly.įrom import FramesFactory, TopocentricFrameįrom import OneAxisEllipsoid, GeodeticPoint Where satellite at A point, observer1 sits B and observer2 sits C.Ī: linear distance between B and C (of course not geodetic) For this, i tried to reduced the problem to “planar geometry”, i.e. First, i would like to check my manifold calculations with using orekit’s built-in Vector3D and TopocentricPoint classes. ![]() I’m working on my problem, and stuck at one point. If observations were “at the same time”, then it could be a way to find a position vector (not a full state vector), which might be useful as a stand-alone function? With non-zero time differences, the error introduced might reasonably be bound by bounding the unknown velocity vector (perhaps specifying the bound in the method API) and evaluating a bounding collection of ranges? ![]() ![]() Perhaps this is the second point made by also doesn’t quite sound like initial OD, because you don’t have enough data. What would be the primary use case for this approach?Īpologies, as I think I might be missing something.Ī new combined “parallax observation” doesn’t sound like something that one would use for precision OD along with other observations - because with sufficient observations we can already use angle data, with parallax correction, accounting for propagation between observations with a very accurate orbital model of our choosing, which self consistently evaluates the parallax for the estimated state vector at the time of observation.
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