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

Abstract

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.

Integrated Design of Autonomous Orbit Determination and Orbit Control for GEO Satellite Based on Neural Network

“…Whether there are still similar conclusions as Ref. [9] needs further research. For the AOD of the GEO satellites, only the intersatellite range between the GEO satellite and the Earth navigation satellites is used as observation, as shown in Figure 1.…”

“…The neural network is used to estimate the unknown perturbation assuming that position can be measured. Gao et al improve the observer to estimate the state of the navigation satellite with only intersatellite ranging [9]. However, the AOD and control of a maneuvered satellite is not considered.…”