Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 36323
- Type
- D - Journal article
- DOI
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10.1016/j.asr.2020.06.021
- Title of journal
- Advances in Space Research
- Article number
- -
- First page
- 617
- Volume
- 67
- Issue
- 1
- ISSN
- 0273-1177
- Open access status
- Deposit exception
- Month of publication
- July
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
H - Computer Vision and Machine Learning Group
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper resulted from a NASA funded (New Frontiers program approx. $7.87m in total) collaboration involving MIT, and the University of Arizona. The research has developed a robust neural algorithm for on-board, real-time calculation of the gravity field near to an asteroid’s surface which is needed for close-proximity spacecraft operations. This ongoing work is intended to reduce the risks involved in future space vehicle landings on asteroids or other planetary bodies with irregular orbital mechanics.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
