IBM and researchers at the University of Texas at Austin are collaborating on an open source project to determine and predict the orbits of anthropogenic space objects (ASOs)—man-made space debris—in order to avoid collisions.
Current methods for orbit prediction rely on physics-based models using location data on ASOs from terrestrial-based sensors, which tends to be imperfect.
The Space Situational Awareness project uses machine learning (ML)-generated models that learn when physical models incorrectly predict an ASO's future location.
The project employs data from the U.S. Strategic Command through the space-track.org website, with IBM hardware running physical models to anticipate the orbits of all ASOs in low earth orbit and train ML models on the physics model error.
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA
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