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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 vs. false stars. (a) Recognition rate with 1 false star; (b) Recognition rate with 2 false stars; (c) Recognition rate with 3 false stars.
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sensors-15-16412-f006: Recognition rate vs. false stars. (a) Recognition rate with 1 false star; (b) Recognition rate with 2 false stars; (c) Recognition rate with 3 false stars.

Mentions: Figure 6 shows the statistical results of the recognition rates of the four algorithms for all 6685 navigation stars with the number of false stars ranging from 1 to 3. In Figure 6, without positional noise, the identification rate of the proposed algorithm decreases from 99.80% to 98.90% when the number of the false stars increases from 1 to 3. Under the same conditions, the rate of the pyramid algorithm decreases from 92.85% to 80.27%, and the rate of the modified grid algorithm decreases from 99% to 86.50%, and the rate of the LPT algorithm decreases from 98.47% to 95.46%. It can be found that the recognition rates of the four algorithms decrease 0.9%, 12.58%, 12.5%, and 3.01%, respectively.


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 vs. false stars. (a) Recognition rate with 1 false star; (b) Recognition rate with 2 false stars; (c) Recognition rate with 3 false stars.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16412-f006: Recognition rate vs. false stars. (a) Recognition rate with 1 false star; (b) Recognition rate with 2 false stars; (c) Recognition rate with 3 false stars.
Mentions: Figure 6 shows the statistical results of the recognition rates of the four algorithms for all 6685 navigation stars with the number of false stars ranging from 1 to 3. In Figure 6, without positional noise, the identification rate of the proposed algorithm decreases from 99.80% to 98.90% when the number of the false stars increases from 1 to 3. Under the same conditions, the rate of the pyramid algorithm decreases from 92.85% to 80.27%, and the rate of the modified grid algorithm decreases from 99% to 86.50%, and the rate of the LPT algorithm decreases from 98.47% to 95.46%. It can be found that the recognition rates of the four algorithms decrease 0.9%, 12.58%, 12.5%, and 3.01%, respectively.

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.