Small bodies non-uniform gravity field on-board learning through Hopfield Neural Networks

Pasquale A.; Silvestrini S.; Capannolo A.Lunghi P.Lavagna M.

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Small bodies non-uniform gravity field on-board learning through Hopfield Neural Networks

机译：Small bodies non-uniform gravity field on-board learning through Hopfield Neural Networks

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Small bodies environment is usually difficult to be modelled for a number of reasons. Among the others, the uncertainty associated to the non-uniform gravitational field requires in-situ observations for its refinement, or its identification. This operation becomes even more challenging in case the orbiting platform is a CubeSat or, in general, a platform with reduced computational power as well as a high autonomy requirement. In this paper, a new approach to reconstruct on-board the gravity field of either unknown or partially known bodies is presented. In particular, the use of a Hopfield Neural Network (HNN) to reconstruct the coefficients of a Spherical Harmonics Expansion (SHE), that is assumed to approximate the gravity field of the body, is described. A comparison with an Extended Kalman Filter (EKF) used for parameter estimation is presented and the differences of the two methods are critically discussed: due to the structure of the HNN, the former results to be computationally faster and lighter than a stand-alone EKF used for parameter estimation.

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《Planetary and space science》 |2022年第3期|105425.1-105425.14|共14页
作者
Pasquale A.; Silvestrini S.; Capannolo A.Lunghi P.Lavagna M.;
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Politecnico di Milano, Via La Masa 34, Milano, 20156, Italy;

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正文语种英语
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Asteroid proximity operations; Gravity field identification; Hopfield neural network (HNN); Online learning; Parameter estimation;

Small bodies non-uniform gravity field on-board learning through Hopfield Neural Networks

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