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 (EKF) 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 (gravity field and steady-state ocean circulation explorer) satellite are used to test the algorithm. Radial and cross-track position errors of less than 100 m have been achieved.
Autonomous orbit determination via kalman filtering of gravity gradients
IEEE Transactions on Aerospace and Electronic Systems ; 52 , 5 ; 2436-2451
2016-10-01
1170556 byte
Article (Journal)
Electronic Resource
English
Hierarchical Adaptive Kalman Filtering for Interplanetary Orbit Determination
Online Contents | 1998
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