Attitude reconstruction of an unknown co-orbiting satellite target using machine learning technologies

Abstract

Active debris removal missions pose demanding guidance, navigation and control requirements. We propose that novel machine learning techniques can help to meet several of the outstanding requirements. Building upon previous work which adopts machine learning technologies for tracking the rotational state of an unknown and uncooperative debris satellite, we improve the approach by further applying machine learning to make use of past measurements. The attitude of the debris target is reconstructed, thereby enabling different debris removal methods. The construction of a simulation framework for generating accurate labelled image data is presented, with the aim of facilitating further research in this area. Finally, we show that a neural network can also learn to track satellites and identify suitable locations for contact-based removal methods, without a-priori knowledge of the object's geometry.

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Identifiers

ORCID for Benjamin, Felix Guthrie: orcid.org/0000-0001-7513-1521

ORCID for Minkwan Kim: orcid.org/0000-0002-6192-312X

ORCID for Jonathon Hare: orcid.org/0000-0003-2921-4283

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