Improvement of star identification algorithms using a database generation method based on spatial correlation of stars

Hamid Aminoroaya; Ali Jabar Rashidi; Mohammad Noori

doi:10.1364/JOSAA.421637

Journal of the Optical Society of America A
Vol. 38,
Issue 8,
pp. 1085-1093
(2021)
•https://doi.org/10.1364/JOSAA.421637

Improvement of star identification algorithms using a database generation method based on spatial correlation of stars

Hamid Aminoroaya, Ali Jabar Rashidi, and Mohammad Noori

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Hamid Aminoroaya, Ali Jabar Rashidi, and Mohammad Noori, "Improvement of star identification algorithms using a database generation method based on spatial correlation of stars," J. Opt. Soc. Am. A 38, 1085-1093 (2021)

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Abstract

A star tracker, in lost-in-space (LIS) and tracking operation modes, applies an accurate algorithm in the star identification phase. The pattern-matching-based star identification algorithms apply patterns to search a prebuilt database. By applying this newly proposed database, it is possible to apply many LIS algorithms in the LIS and tracking modes. Modifying accurate LIS mode algorithms through this proposed method and applying them in the tracking mode would improve reliability, accuracy, and speed. The simulation results indicate that this proposed approach would reduce the search time for all applied algorithms by a tens factor based on the algorithm’s specifications.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Subgraph Isomorphism		Artificial Intelligence		Pattern Recognition		Heuristic Algorithms
Liebe [4]	1995	Roberts and Walker [11]	2005	Padgett and Kreutz-Delgado [14]	1997	Juang et al. [21]	2003
Van Bezooijen [5]	1998	Paladugu et al. [12]	2006	Clouse and Padgett [15]	2000	Mcvittie [22]	2013
Mortari et al. [6]	2001	Quan and Fang [13]	2010	Silani and Lovera [16]	2006	Wei et al. [23]	2013
Zhang et al. [7]	2006			Lee and Bang [17]	2007	Yoon et al. [24]	2015
Kolomenkin et al. [8]	2008			Na et al. [18]	2009	Sun et al. [25]	2016
Hernandez [9]	2016			Aghaei and Moghaddam [19]	2017	Wang et al. [26]	2017
Schiattarella et al. [10]	2017			Wei et al. [20]	2019	Samirbhai et al. [27]	2018

Cluster #	Star ID	Patterns	Neighboring Clusters
Cluster 1	42637	$⟨$ Pattern of 42637 $⟩$	&Cluster 2, &Cluster 3, &Cluster 4, &Cluster 5, &Cluster 6, &Cluster 7
	43012	$⟨$ Pattern of 43012 $⟩$
	41191	$⟨$ Pattern of 41191 $⟩$
	⋮	⋮
Cluster 2	14131	$⟨$ Pattern of 14131 $⟩$	&Cluster 48, &Cluster 62, &Cluster 69, &Cluster 3, &Cluster 1, &Cluster 7
	12273	$⟨$ Pattern of 12273 $⟩$
	14862	$⟨$ Pattern of 14862 $⟩$
	⋮	⋮
Cluster n	…	…	…
	…	…
	…	…

Parameter	Value
Frame rate	5 Hz
FOV size	8 deg
Visible magnitude limit	6
Catalogue stars	5037

Subgraph Isomorphism		Artificial Intelligence		Pattern Recognition		Heuristic Algorithms
Liebe [4]	1995	Roberts and Walker [11]	2005	Padgett and Kreutz-Delgado [14]	1997	Juang et al. [21]	2003
Van Bezooijen [5]	1998	Paladugu et al. [12]	2006	Clouse and Padgett [15]	2000	Mcvittie [22]	2013
Mortari et al. [6]	2001	Quan and Fang [13]	2010	Silani and Lovera [16]	2006	Wei et al. [23]	2013
Zhang et al. [7]	2006			Lee and Bang [17]	2007	Yoon et al. [24]	2015
Kolomenkin et al. [8]	2008			Na et al. [18]	2009	Sun et al. [25]	2016
Hernandez [9]	2016			Aghaei and Moghaddam [19]	2017	Wang et al. [26]	2017
Schiattarella et al. [10]	2017			Wei et al. [20]	2019	Samirbhai et al. [27]	2018

Cluster #	Star ID	Patterns	Neighboring Clusters
Cluster 1	42637	$⟨$ Pattern of 42637 $⟩$	&Cluster 2, &Cluster 3, &Cluster 4, &Cluster 5, &Cluster 6, &Cluster 7
	43012	$⟨$ Pattern of 43012 $⟩$
	41191	$⟨$ Pattern of 41191 $⟩$
	⋮	⋮
Cluster 2	14131	$⟨$ Pattern of 14131 $⟩$	&Cluster 48, &Cluster 62, &Cluster 69, &Cluster 3, &Cluster 1, &Cluster 7
	12273	$⟨$ Pattern of 12273 $⟩$
	14862	$⟨$ Pattern of 14862 $⟩$
	⋮	⋮
Cluster n	…	…	…
	…	…
	…	…

Abstract

Data Availability

Cited By

Figures (10)

Tables (3)

Equations (6)

Journal of the Optical Society of America A