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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 rates vs. positional noise (F: false star, L: lost star).
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sensors-15-16412-f005: Recognition rates vs. positional noise (F: false star, L: lost star).

Mentions: It can be known from Figure 5 that the recognition rate of the proposed algorithm is higher than that of the other three algorithms, and the downtrend of the recognition rate of the LPT algorithm is greater than that of the other three algorithms. With the increase of the positional noise, the recognition rate of the proposed algorithm decreases just about 1.37%, while the recognition rates of the pyramid algorithm, the modified grid algorithm, and the LPT algorithm decrease about 3.06%, 1.43%, and 7.89%, respectively. From the analysis data, it can be found that the downtrend of the modified grid algorithm is similar to that of the proposed algorithm with the increase of the positional noise, and the LPT algorithm is sensitive to the positional noise which has the biggest decline of 7.89%.


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

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

Recognition rates vs. positional noise (F: false star, L: lost star).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16412-f005: Recognition rates vs. positional noise (F: false star, L: lost star).
Mentions: It can be known from Figure 5 that the recognition rate of the proposed algorithm is higher than that of the other three algorithms, and the downtrend of the recognition rate of the LPT algorithm is greater than that of the other three algorithms. With the increase of the positional noise, the recognition rate of the proposed algorithm decreases just about 1.37%, while the recognition rates of the pyramid algorithm, the modified grid algorithm, and the LPT algorithm decrease about 3.06%, 1.43%, and 7.89%, respectively. From the analysis data, it can be found that the downtrend of the modified grid algorithm is similar to that of the proposed algorithm with the increase of the positional noise, and the LPT algorithm is sensitive to the positional noise which has the biggest decline of 7.89%.

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.