Blind Star Identification Algorithm

Somayehee, Farshad; Nikkhah, Amir Ali; Roshanian, Jafar; Salahshoor, Sadegh

doi:10.1109/taes.2019.2917572

Cited by 12 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The non-dimensional algorithm presented here is reminiscent of the algorithms presented in References [ 9 , 10 , 11 , 12 , 13 , 16 ]. Nevertheless, the non-dimensional algorithm introduced in this work has three major differences when compared with these previous algorithms: The NDSIA identifies stars using the dihedral angles of spherical star triangles.…”

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

“…Nevertheless, the non-dimensional algorithm introduced in this work has three major differences when compared with these previous algorithms: The NDSIA identifies stars using the dihedral angles of spherical star triangles. Previous algorithms used angles of planar star triangles [ 9 , 10 , 11 , 12 ], the sides of planar star triangles [ 13 ], or asterisms composed of four or five stars [ 16 ]. The NDSIA uses the n -dimensional k -vector (NDKV) [ 14 ] to search among a three-dimensional database.…”

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

“… The NDSIA uses the n -dimensional k -vector (NDKV) [ 14 ] to search among a three-dimensional database. Previous algorithms search among a one- or two-dimensional database [ 9 , 10 , 11 , 12 , 13 ], using searching techniques such as the one-dimensional k -vector [ 3 ], or by geometrically hashing asterisms of stars and using a hash-based search algorithm [ 16 ]. The NDSIA performs a final check that uses the identified stars’ interstellar angles to further the confidence in the Star-ID.…”

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

“…The NDSIA identifies stars using the dihedral angles of spherical star triangles. Previous algorithms used angles of planar star triangles [ 9 , 10 , 11 , 12 ], the sides of planar star triangles [ 13 ], or asterisms composed of four or five stars [ 16 ].…”

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

“…To that end, previous algorithms [ 9 , 10 , 11 , 12 , 13 ] have proposed solutions based on the non-dimensional Star-ID problem, i.e., the identification of stars when the focal length or OA offset of the star tracker were perturbed from their nominal values. However, these algorithms present an important limitation: the non-dimensional database used to obtain the Star-ID must be small.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Non-Dimensional Star-Identification

Leake

Arnas

Mortari

2020

Sensors

View full text Add to dashboard Cite

This study introduces a new “Non-Dimensional” star identification algorithm to reliably identify the stars observed by a wide field-of-view star tracker when the focal length and optical axis offset values are known with poor accuracy. This algorithm is particularly suited to complement nominal lost-in-space algorithms, which may identify stars incorrectly when the focal length and/or optical axis offset deviate from their nominal operational ranges. These deviations may be caused, for example, by launch vibrations or thermal variations in orbit. The algorithm performance is compared in terms of accuracy, speed, and robustness to the Pyramid algorithm. These comparisons highlight the clear advantages that a combined approach of these methodologies provides.

show abstract

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

Section: The Non-dimensional Star-id Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Non-Dimensional Star-Identification

Leake

Arnas

Mortari

2020

Sensors

View full text Add to dashboard Cite

show abstract

Certificateless Blind Proxy Signature Algorithm Based on PSS Standard Model

Liu

You

2023

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Yang²,

Xiao

et al. 2022

J. Navigation

View full text Add to dashboard Cite

The fish-eye star sensor with a field of view (FOV) of 180° is an important piece of equipment for attitude determination, which improves the visibility of stars significantly. However, it also brings the star identification (star-ID) difficulties because of imprecise calibrations. Thus, a fish-eye star-ID algorithm supported by the integration of the precise point positioning/inertial navigation system (PPP/INS) is proposed. At first, a reference star map is generated in combination with the distortion model of the fish-eye camera based on the position and attitude information from the PPP/INS. Then the star points are extracted in a specific neighbourhood of the reference star points. Subsequently, the extracted star points are individually tested and identified according to angular distance error. Finally, the real-time precise attitude is determined based on the star-ID results. Experimental results show that, 270–310 stars can be identified in a fish-eye star map with an average time of 0.03 s if the initial attitude error is smaller than 1.5° and an attitude determination accuracy better than 10″ can be achieved by support from PPP/INS.

show abstract

Blind Star Identification Algorithm

Cited by 12 publications

References 16 publications

Non-Dimensional Star-Identification

Non-Dimensional Star-Identification

Certificateless Blind Proxy Signature Algorithm Based on PSS Standard Model

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Contact Info

Product

Resources

About