2007
DOI: 10.3311/pp.ci.2007-1.03
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Analysis of numerical differentiation methods applied for determination of kinematic velocities for LEOs

Abstract: Kinematic orbits provide a time series of independent positions, which are a good base for gravity field recovery. Gravity field recovery using the energy integral requires numerical differentiation in order to get velocity information for kinetic energy. This paper deals with numerical differentiation methods to test the most effective method for velocity determination of a LEO (Low Earth Orbiter).

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Cited by 8 publications
(5 citation statements)
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“…10 to the power −3 mm 2 /s 2 /Hz signal, which is at this frequency equivalent to 0.003-0.01 mm/s. This is consistent in magnitude with the RMS differences of the kinematic velocities in Table 7 of [4].…”
Section: Discussion and Summarysupporting
confidence: 78%
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“…10 to the power −3 mm 2 /s 2 /Hz signal, which is at this frequency equivalent to 0.003-0.01 mm/s. This is consistent in magnitude with the RMS differences of the kinematic velocities in Table 7 of [4].…”
Section: Discussion and Summarysupporting
confidence: 78%
“…These were: dynamic orbit using EIGEN-1S model [7], dynamic orbit using TEG-4 model (both up to degree and order of 120), reduced-dynamic orbit based EIGEN-2 gravity model [8]. In [4] the dynamic and reduced-dynamic positions were compared to the kinematic positions; the RMS of the positions were 1.1233 m, 1.1091 m and 0.0311 m for the dynamic EIGEN-1S, dynamic TEG-4 and reduced-dynamic EIGEN-2 orbits, respectively. A kind of comparison also has been done for the velocities: the EIGEN-1S and the TEG-4 velocities differ from the reduced-dynamic velocities with an RMS of 1.5826 mm/s and 1.3562 mm/s, respectively.…”
Section: The Datamentioning
confidence: 99%
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“…However, velocity vector is required for more applications e.g. in gravity field recovery using the energy integral [8]. The numerical differentiation is a mathematical process for computing the numerical value of derivative of a function [9].…”
Section: Numerical Differentiationmentioning
confidence: 99%