Autonomous Orbit Determination via Kalman Filtering of Gravity Gradients

TL;DR

This paper presents a method using Kalman filtering of gravity gradient data for autonomous orbit determination of near Earth satellites, demonstrating high accuracy with real satellite data and analyzing various error factors.

Contribution

It introduces an extended Kalman filter approach utilizing gravity gradients for autonomous orbit determination, including an augmented state filter for bias correction.

Findings

Achieved less than 100 m radial and cross-track position errors with real data.

The filter performs well with white noise observation errors.

Degraded observability observed for along-track position in augmented filter.

Abstract

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.