ORBIT-CENTERED ATMOSPHERIC DENSITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS
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At low Earth orbits, drag force is a significant source of error for propagating the motion of spacecraft. The main factor driving changes on the drag force is the 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 re- fers 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 predic- tor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Compar- ison with previously developed methods show substantial benefits in using ar- tificial 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.
accepted, to appear on ACTA Astronautica.The article of record may be found at http://dx.doi.org/10.1016/j.actaastro.2014.01.007. Author's Accepted Manuscript. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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