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An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

Luo L, Xu L, Zhang H - Sensors (Basel) (2015)

Bottom Line: The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors.The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly.The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible.

View Article: PubMed Central - PubMed

Affiliation: School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China. liyanluo@stu.xidian.edu.cn.

ABSTRACT
In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

No MeSH data available.


Recognition rate with different neighborhood radiuses R.
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sensors-15-16412-f004: Recognition rate with different neighborhood radiuses R.

Mentions: In this section, we discuss the recognition accuracy of the proposed algorithm under different neighborhood radiuses. Figure 4 shows the statistics results without any noise that every navigation star is adopted as the direction of the optical axis when R ranges from 3° to 10°. It can be found from Figure 4 that the recognition rate of the proposed algorithm changes with the increase of R. The recognition rate is very low when R is small, and subsequently the recognition rate increases with the increase of R. It achieves the largest recognition rate when R = 6°, then the recognition rate falls down with the increase of R. In the subsequent tests, the neighborhood radius is set to be R = 6°.


An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

Luo L, Xu L, Zhang H - Sensors (Basel) (2015)

Recognition rate with different neighborhood radiuses R.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541885&req=5

sensors-15-16412-f004: Recognition rate with different neighborhood radiuses R.
Mentions: In this section, we discuss the recognition accuracy of the proposed algorithm under different neighborhood radiuses. Figure 4 shows the statistics results without any noise that every navigation star is adopted as the direction of the optical axis when R ranges from 3° to 10°. It can be found from Figure 4 that the recognition rate of the proposed algorithm changes with the increase of R. The recognition rate is very low when R is small, and subsequently the recognition rate increases with the increase of R. It achieves the largest recognition rate when R = 6°, then the recognition rate falls down with the increase of R. In the subsequent tests, the neighborhood radius is set to be R = 6°.

Bottom Line: The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors.The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly.The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible.

View Article: PubMed Central - PubMed

Affiliation: School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China. liyanluo@stu.xidian.edu.cn.

ABSTRACT
In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

No MeSH data available.