New star identification algorithm using labelling technique

Kim, Sangkyun; Cho, Mengu

doi:10.1016/j.actaastro.2019.06.007

Cited by 7 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of these algorithms use the principle of map recognition to construct a unique feature for each observation star. This kind of algorithm is often composed of the geometric distribution characteristics of other stars in a certain neighbourhood of the star, and primarily includes the grid, singular-value, genetic, list and marking algorithms (Padgett and Kreutz-Delgado, 1997; Juang et al., 2003; Sun et al., 2016; Mehta et al., 2018; Kim and Cho, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Section: Introductionmentioning

confidence: 99%

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

show abstract

“…However, the star-identification rate is prone to be low when there is magnitude noise. There are also other pattern-based algorithms proposed to improve robustness, such as the recommended radial pattern [13], log-polar transform [14], redundant-coded pattern [15], label code [16], and triangle map matrix methods [17], but their performance is still poor when there is magnitude noise.…”

Section: Introductionmentioning

confidence: 99%

A Star-Identification Algorithm Based on Global Multi-Triangle Voting

Yuan

Zhu

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

A star-identification algorithm aimed at identifying imaged stars in a “lost in space” scene, named the global multi-triangle voting algorithm (GMTV), is presented in this paper. There are two core parts included in the proposed algorithm: in the initial match part, triangle feature units are treated as vote units to find the initial match relationship via matching vote units and counting the vote number of each catalog star. During this step, the principal component analysis (PCA) method is implemented to reduce feature dimensions, and a two-dimension lookup table and fuzzy match strategy are utilized to promote database searching efficiency and noise tolerance. After acquiring the initial match results, a verification part is implemented to filter potential errors from initial candidates by the largest cluster method and output the final identification results. The proposed algorithm achieves a 98.6% identification rate with 2.0 pixels position noise and exhibits more robustness to position noise, magnitude noise, and false stars of different levels than the two reference algorithms used in simulations. In addition, our algorithm’s real-time performance is better than reference algorithms, but it requires a larger database.

show abstract

“…Algorithms of the second category assign each star a pattern based on the relative positioning to neighbouring stars and try to find the closest match to the measured pattern in the pattern database. These include grid algorithms [13], the singular value method algorithm [14], the Log-Polar transform algorithm [15], the Hidden Markov Model based algorithm [16], the genetic algorithm based identification algorithm [17], the K-L transformation algorithm [18], the ordered set of points algorithm [19] and the labelling technique algorithm [20]. Although some papers suggest a third category for novel algorithms using techniques such as neural networks or genetic algorithms, these can also be classified using the categories above since this classification is based on feature extraction methods [21].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Lost-in-Space Star Identification Algorithms Since 2009

Rijlaarsdam

Yous

Byrne

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

New star identification algorithm using labelling technique

Cited by 7 publications

References 12 publications

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

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

A Star-Identification Algorithm Based on Global Multi-Triangle Voting

A Survey of Lost-in-Space Star Identification Algorithms Since 2009

Contact Info

Product

Resources

About