Airborne Vector Gravimetry Using GPS/INS
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Date
2000-04
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Ohio State University. Division of Geodetic Science
Abstract
Compared to the conventional ground measurement of gravity, airborne
gravimetry is relatively efficient and cost-effective. Especially, the combination of GPS
and INS is known to show very good performances in the range of medium frequencies
(1-100 km) for recovering the gravity signal.
Conventionally, gravity estimation using GPS/INS was analyzed through the
estimation of INS system errors using GPS position and velocity updates. In this case,
the complex navigation equations must be integrated to obtain the INS position, and
the gravity field must be stochastically modeled as a part of the state vector. The
vertical component of the gravity vector is not estimable in this case because of the
instability of the vertical channel in the solution of the inertial navigation equations.
In this study, a new algorithm using acceleration updates instead of
position/velocity updates has been developed. Because we are seeking the gravitational
field, that is, accelerations, the new approach is conceptually simpler and more
straightforward. In addition, it is computationally less expensive since the navigation
equations do not have to be integrated. It is more objective, since the gravity
disturbance field does not have to be explicitly modeled as state parameters.
An application to real test flight data as well as an intensive simulation study has
been performed to test the validity of the new algorithm. The results from the real
flight data show very good accuracy in determining the down component, with
accuracy better than ±5 mGal. Also, a comparable result was obtained for the
horizontal components with accuracy of ±6 to ±8 mGal. The resolution of the final
result is about 10 km due to the attenuation with altitude.
The inclusion of a parametric gravity model into the new algorithm is also
investigated for theoretical reasons. The gravity estimates from this filter showed
strong dependencies on the model and required extensive computation with no
improvement over the approach without parametric gravity model.
Description
This report was prepared by Jay Hyoun Kwon, a graduate student, Department of Civil and Environmental Engineering and Geodetic Science, under the supervision of Professor Christopher Jekeli.
This research was supported by the National Imagery and Mapping Agency (NIMA); Contract No. NMA202-98-1-1110.
It was submitted to the Graduate School of The Ohio State University in the Winter of 2000 in partial fulfillment of the requirements of the Doctor of Philosophy degree.
This research was supported by the National Imagery and Mapping Agency (NIMA); Contract No. NMA202-98-1-1110.
It was submitted to the Graduate School of The Ohio State University in the Winter of 2000 in partial fulfillment of the requirements of the Doctor of Philosophy degree.