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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.


The vector direction of the star pattern. (a) The alignment star; (b) The stellar image is rotated.
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sensors-15-16412-f002: The vector direction of the star pattern. (a) The alignment star; (b) The stellar image is rotated.

Mentions: Aiming at the improvement of the speed of star identification, the one-to-one relationship between the navigation star and its one-dimensional vector pattern is built in the proposed algorithm. In order to achieve the unique pattern of the navigation star, the centroid coordinates of stars observed in FOV are reset. In the process of star identification, one of the observed stars is chosen as the main star, and the observed stars located in the neighboring region with a radius of R (see Figure 2) are called the neighbor stars of the main star. The star pattern of the main star consists of the main star and its neighbor stars. The nearest neighbor star is regarded as the alignment star of the main star. The direction from the main star to the alignment star is considered as the vector direction of the star pattern.


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

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

The vector direction of the star pattern. (a) The alignment star; (b) The stellar image is rotated.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16412-f002: The vector direction of the star pattern. (a) The alignment star; (b) The stellar image is rotated.
Mentions: Aiming at the improvement of the speed of star identification, the one-to-one relationship between the navigation star and its one-dimensional vector pattern is built in the proposed algorithm. In order to achieve the unique pattern of the navigation star, the centroid coordinates of stars observed in FOV are reset. In the process of star identification, one of the observed stars is chosen as the main star, and the observed stars located in the neighboring region with a radius of R (see Figure 2) are called the neighbor stars of the main star. The star pattern of the main star consists of the main star and its neighbor stars. The nearest neighbor star is regarded as the alignment star of the main star. The direction from the main star to the alignment star is considered as the vector direction of the star pattern.

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.