  Spaceborne gravity gradients are proposed in this paper to provide autonomous
orbit determination capabilities for near Earth satellites. The gravity
gradients contain useful position information which can be extracted by
matching the observations with a precise gravity model. The extended Kalman
filter is investigated as the principal estimator. The stochastic model of
orbital motion, the measurement equation and the model configuration are
discussed for the filter design. An augmented state filter is also developed to
deal with unknown significant measurement biases. Simulations are conducted to
analyze the effects of initial errors, data-sampling periods, orbital heights,
attitude and gradiometer noise levels, and measurement biases. Results show
that the filter performs well with additive white noise observation errors.
Degraded observability for the along-track position is found for the augmented
state filter. Real flight data from the GOCE satellite are used to test the
algorithm. Radial and cross-track position errors of less than 100 m have been
achieved.
