Orbit-centered atmospheric density prediction using artificial neural networks

• We address the problem of predicting drag/neutral density on a given orbit.
• Neural networks were used to create localized density predictors.
• The predictors can be used for accurate maneuver planning.
• Such a method holds potential for future onboard implementation on real spacecraft.

At low Earth orbits, drag force is a significant source of error for propagating the motion of a spacecraft. The main factor driving the changes on the drag force is neutral density. Global atmospheric models provide estimates for the density which are significantly affected by bias due to misrepresentations of the underlying physics and limitations on the statistical models. In this work a localized predictor based on artificial neural networks is presented. Localized refers to the focus being on a specific orbit, rather than a global prediction. The predictor uses density measurements or estimates on a given orbit and a set of proxies for solar and geomagnetic activities to predict the value of the density along the future orbit of the spacecraft. The performance of the localized predictor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Comparison with previously developed methods show substantial benefits in using artificial neural networks, both in prediction accuracy and in the potential for spacecraft onboard implementation. In fact, the proposed neural networks are computationally efficient and would be straightforward to integrate into onboard software.