A Survey of Lost-in-Space Star Identification Algorithms since 2009.
attitude estimation
star feature extraction
star identification
star tracker algorithms
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
01 May 2020
01 May 2020
Historique:
received:
06
03
2020
revised:
13
04
2020
accepted:
25
04
2020
entrez:
7
5
2020
pubmed:
7
5
2020
medline:
7
5
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32369986
pii: s20092579
doi: 10.3390/s20092579
pmc: PMC7248786
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Références
Sensors (Basel). 2010;10(3):1955-66
pubmed: 22294908