Small bodies non-uniform gravity field on-board learning through Hopfield Neural Networks
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Research works printed but not linked: 18
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Silvestrini, S., Piccinin, M., Zanotti, G., Brandonisio, A., Bloise, I., Feruglio, L., Lunghi, P., Lavagna, M., & Varile, M. (2022) · Aerospace Science and Technology · doi:10.1016/j.ast.2022.107503 · Cited by 65 · see citing works
Self-citation
- Authors
- Pasquale, A., Silvestrini, S., Capannolo, A., Lunghi, P., & Lavagna, M.
- Affiliations
- Politecnico di Milano (IT)
- Corresponding author
- Pasquale, Andrea
- Venue
- Planetary and Space Science
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 212
- Pages
- 105425
- ISSN
- 0032-0633 (ISSN-L)
- OpenAlex
- W4206991072
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 3 · 2024: 2 · 2023: 2 · 2022: 3
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Planetary and Space Science (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Design and Technology
- Keywords
- small bodies, Hopfield neural network, spherical harmonic expansion, CubeSat, parameter estimation, extended Kalman filter, computational efficiency, autonomous spacecraft
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