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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.


Stellar image in star sensor. (a) Celestial sphere reference frame; (b) The imaging principle in star sensor; (c) The stars observed in FOV.
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sensors-15-16412-f001: Stellar image in star sensor. (a) Celestial sphere reference frame; (b) The imaging principle in star sensor; (c) The stars observed in FOV.

Mentions: The star catalog and astronomical almanac express a star’s position in terms of its right ascension α and declination β in the celestial sphere reference frame (see Figure 1a). The parallel light from stars is imaging on the focal plane of the charge-coupled device (CCD) in star sensor, so the position of the star in the celestial sphere reference frame is expressed in terms of pixels along the x and y axes on the stellar image (see Figure 1b). The light points on the stellar image are the stars observed in FOV (see Figure 1c), which are the research objects in star identification. Before the experiment, it needs to simulate the stellar image according to the information of the navigation stars in the star catalogue.


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

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

Stellar image in star sensor. (a) Celestial sphere reference frame; (b) The imaging principle in star sensor; (c) The stars observed in FOV.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16412-f001: Stellar image in star sensor. (a) Celestial sphere reference frame; (b) The imaging principle in star sensor; (c) The stars observed in FOV.
Mentions: The star catalog and astronomical almanac express a star’s position in terms of its right ascension α and declination β in the celestial sphere reference frame (see Figure 1a). The parallel light from stars is imaging on the focal plane of the charge-coupled device (CCD) in star sensor, so the position of the star in the celestial sphere reference frame is expressed in terms of pixels along the x and y axes on the stellar image (see Figure 1b). The light points on the stellar image are the stars observed in FOV (see Figure 1c), which are the research objects in star identification. Before the experiment, it needs to simulate the stellar image according to the information of the navigation stars in the star catalogue.

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.