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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.


Generation of the one-dimensional vector pattern. (a) The positions of the observed stars on oʹxʹ axis; (b) The plane included angles.
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sensors-15-16412-f003: Generation of the one-dimensional vector pattern. (a) The positions of the observed stars on oʹxʹ axis; (b) The plane included angles.

Mentions: In order to enhance the robustness of the proposed algorithm, the oʹxʹ axis within the scope of 2R is divided at regular intervals (see Figure 3b). Set the resolution of the oʹxʹ axis locating in the neighboring region of the main star be m, so the interval on the oʹxʹ axis will be 2R/m. The feature vector of the main star can be achieved according to the results of the one-dimensional vector pattern. So the feature vector of the main star can be expressed as(8)pat(s)=(a1,a2…,am)={aj},j=1,…,m


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

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

Generation of the one-dimensional vector pattern. (a) The positions of the observed stars on oʹxʹ axis; (b) The plane included angles.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16412-f003: Generation of the one-dimensional vector pattern. (a) The positions of the observed stars on oʹxʹ axis; (b) The plane included angles.
Mentions: In order to enhance the robustness of the proposed algorithm, the oʹxʹ axis within the scope of 2R is divided at regular intervals (see Figure 3b). Set the resolution of the oʹxʹ axis locating in the neighboring region of the main star be m, so the interval on the oʹxʹ axis will be 2R/m. The feature vector of the main star can be achieved according to the results of the one-dimensional vector pattern. So the feature vector of the main star can be expressed as(8)pat(s)=(a1,a2…,am)={aj},j=1,…,m

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.