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Report

Towards Automated Satellite Conjunction Management with Bayesian Deep Learning

Autor: Pinto, Francesco, Acciarini, Giacomo, Metz, Sascha, Boufelja, Sarah, Kaczmarek, Sylvester, Merz, Klaus, Martinez-Heras, José A., Letizia, Francesca, Bridges, Christopher, Baydin, Atılım Güneş

Publikováno v: AI for Earth Sciences Workshop at NeurIPS 2020, Vancouver, Canada

After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions. Objects in high enough altitudes do not re-enter and burn up in the atmosphere,

Externí odkaz: http://arxiv.org/abs/2012.12450

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Spacecraft Collision Risk Assessment with Probabilistic Programming

Autor: Acciarini, Giacomo, Pinto, Francesco, Metz, Sascha, Boufelja, Sarah, Kaczmarek, Sylvester, Merz, Klaus, Martinez-Heras, José A., Letizia, Francesca, Bridges, Christopher, Baydin, Atılım Güneş

Publikováno v: Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada

Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat t

Externí odkaz: http://arxiv.org/abs/2012.10260

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Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition

Autor: Uriot, Thomas, Izzo, Dario, Sim{õ}es, Luís F., Abay, Rasit, Einecke, Nils, Rebhan, Sven, Martinez-Heras, Jose, Letizia, Francesca, Siminski, Jan, Merz, Klaus

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation me

Externí odkaz: http://arxiv.org/abs/2008.03069

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