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Results from the Simulations of Geopotential Coefficient Estimation from Gravity Gradients

  • S. Bettadpur
  • B. E. Schutz
  • J. B. Lundberg
Conference paper
Part of the International Association of Geodesy Symposia book series (IAG SYMPOSIA, volume 110)

Abstract

New information of the short and medium wavelength components of the geopotential is expected from the measurements of gravity gradients made by the future European Space Agency (ESA) Aristoteles and the NASA Superconducting Gravity Gradiometer (SGG) missions. In this paper, results are presented from preliminary simulations concerning the estimation of the spherical harmonic coefficients of the geopotential expansion from gravity gradients data.

Numerical issues in the Brute-Force Inversion (BFI) of the gravity gradients data are examined first. Numerical algorithms have been developed that substantially speed up the computation of the potential, acceleration and gradients, as well as the mapping from the gravity gradients to the geopotential coefficients. The solution of a large least squares problem is also examined and computational requirements determined for the implementation of a large scale inversion.

A comparative analysis of the results from the BFI and a Symmetry Method (SM) is reported for the test simulations of the estimation of a degree and order 50 gravity field. The results from the two, in the presence of white, measurement noise, are seen to compare well. The latter method is implemented on a special, axially symmetric surface that fits the orbit within 380 meters.

Finally, the issue of aliasing between the low, 27 by 27 expansion of the geopotential and the higher, up to 50 by 50 gravity field is examined. It is shown that it may be inadvisable to use gravity fields with currently available accuracies to remove the signal due to the long wavelength geopotential from the observed gravity gradients.

Keywords

Gravity Field European Space Agency Gravity Gradient Orbit Error Spherical Harmonic Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1992

Authors and Affiliations

  • S. Bettadpur
    • 1
  • B. E. Schutz
    • 1
  • J. B. Lundberg
    • 1
  1. 1.Center for Space ResearchThe University of Texas at AustinAustinUSA

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