Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer
| Autor: | Bo Xu, Youtao Gao, Bingyu Jin, Tanran Zhao, Junkang Chen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Physics
020301 aerospace & aeronautics Article Subject Artificial neural network lcsh:Motor vehicles. Aeronautics. Astronautics Stochastic matrix Aerospace Engineering Perturbation (astronomy) 02 engineering and technology 01 natural sciences Computer Science::Robotics 0203 mechanical engineering Control theory Physics::Space Physics 0103 physical sciences Satellite navigation Satellite State observer lcsh:TL1-4050 Orbit determination 010303 astronomy & astrophysics Physics::Atmospheric and Oceanic Physics Constellation |
| Zdroj: | International Journal of Aerospace Engineering, Vol 2017 (2017) |
| ISSN: | 1687-5974 1687-5966 |
| DOI: | 10.1155/2017/9734164 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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