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A Survey of Lost-in-Space Star Identification Algorithms since 2009.
Rijlaarsdam, David; Yous, Hamza; Byrne, Jonathan; Oddenino, Davide; Furano, Gianluca; Moloney, David.
Afiliação
  • Rijlaarsdam D; Intel Corporation, Intel R&D Ireland Ltd., Collinstown, Collinstown Industrial Park, Co. Kildare, W23CW68 Collinstown, Ireland.
  • Yous H; Intel Corporation, Intel R&D Ireland Ltd., Collinstown, Collinstown Industrial Park, Co. Kildare, W23CW68 Collinstown, Ireland.
  • Byrne J; Intel Corporation, Intel R&D Ireland Ltd., Collinstown, Collinstown Industrial Park, Co. Kildare, W23CW68 Collinstown, Ireland.
  • Oddenino D; European Space Agency/ESTEC, 1 Keplerlaan 2201AZ, 3067 Noordwijk, The Netherlands.
  • Furano G; European Space Agency/ESTEC, 1 Keplerlaan 2201AZ, 3067 Noordwijk, The Netherlands.
  • Moloney D; Intel Corporation, Intel R&D Ireland Ltd., Collinstown, Collinstown Industrial Park, Co. Kildare, W23CW68 Collinstown, Ireland.
Sensors (Basel) ; 20(9)2020 May 01.
Article em En | MEDLINE | ID: mdl-32369986
The lost-in-space star identification algorithm is able to identify stars without a priori attitude information and is arguably the most critical component of a star sensor system. In this paper, the 2009 survey by Spratling and Mortari is extended and recent lost-in-space star identification algorithms are surveyed. The covered literature is a qualitative representation of the current research in the field. A taxonomy of these algorithms based on their feature extraction method is defined. Furthermore, we show that in current literature the comparison of these algorithms can produce inconsistent conclusions. In order to mitigate these inconsistencies, this paper lists the considerations related to the relative performance evaluation of these algorithms using simulation.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Qualitative_research Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Qualitative_research Idioma: En Ano de publicação: 2020 Tipo de documento: Article