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International Journal of Aerospace Engineering
Volume 2017 (2017), Article ID 9734164, 10 pages
Research Article

Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer

1College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2College of Astronomy and Space Science, Nanjing University, Nanjing, China

Correspondence should be addressed to Youtao Gao; nc.ude.aaun@oagty

Received 1 March 2017; Revised 11 May 2017; Accepted 24 May 2017; Published 21 June 2017

Academic Editor: Paolo Tortora

Copyright © 2017 Youtao Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to improve the accuracy of the dynamical model used in the orbit determination of the Lagrangian navigation satellites, the nonlinear perturbations acting on Lagrangian navigation satellites are estimated by a neural network. A neural network based state observer is applied to autonomously determine the orbits of Lagrangian navigation satellites using only satellite-to-satellite range. This autonomous orbit determination method does not require linearizing the dynamical mode. There is no need to calculate the transition matrix. It is proved that three satellite-to-satellite ranges are needed using this method; therefore, the navigation constellation should include four Lagrangian navigation satellites at least. Four satellites orbiting on the collinear libration orbits are chosen to construct a constellation which is used to demonstrate the utility of this method. Simulation results illustrate that the stable error of autonomous orbit determination is about 10 m. The perturbation can be estimated by the neural network.