High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm
In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking i...
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/8/2647 |
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| author | Jianming Zhang Junxiang Lian Zhaoxiang Yi Shuwang Yang Ying Shan |
| author_facet | Jianming Zhang Junxiang Lian Zhaoxiang Yi Shuwang Yang Ying Shan |
| author_sort | Jianming Zhang |
| collection | DOAJ |
| description | In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites. |
| first_indexed | 2024-03-10T12:26:43Z |
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| id | doaj.art-1d9b30f11fc641519ca21c6ac31135b8 |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:26:43Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1d9b30f11fc641519ca21c6ac31135b82023-11-21T14:55:23ZengMDPI AGSensors1424-82202021-04-01218264710.3390/s21082647High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification AlgorithmJianming Zhang0Junxiang Lian1Zhaoxiang Yi2Shuwang Yang3Ying Shan4MOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics & School of Physics and Astronomy, Frontiers Science Center for TianQin, CNSA Research Center for Gravitational Waves, Zhuhai Campus, Sun Yat-Sen University, Zhuhai 519082, ChinaMOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics & School of Physics and Astronomy, Frontiers Science Center for TianQin, CNSA Research Center for Gravitational Waves, Zhuhai Campus, Sun Yat-Sen University, Zhuhai 519082, ChinaMOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics & School of Physics and Astronomy, Frontiers Science Center for TianQin, CNSA Research Center for Gravitational Waves, Zhuhai Campus, Sun Yat-Sen University, Zhuhai 519082, ChinaMOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics & School of Physics and Astronomy, Frontiers Science Center for TianQin, CNSA Research Center for Gravitational Waves, Zhuhai Campus, Sun Yat-Sen University, Zhuhai 519082, ChinaMOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics & School of Physics and Astronomy, Frontiers Science Center for TianQin, CNSA Research Center for Gravitational Waves, Zhuhai Campus, Sun Yat-Sen University, Zhuhai 519082, ChinaIn order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites.https://www.mdpi.com/1424-8220/21/8/2647star sensorguide star cataloguemachine learning |
| spellingShingle | Jianming Zhang Junxiang Lian Zhaoxiang Yi Shuwang Yang Ying Shan High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm Sensors star sensor guide star catalogue machine learning |
| title | High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm |
| title_full | High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm |
| title_fullStr | High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm |
| title_full_unstemmed | High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm |
| title_short | High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm |
| title_sort | high accuracy guide star catalogue generation with a machine learning classification algorithm |
| topic | star sensor guide star catalogue machine learning |
| url | https://www.mdpi.com/1424-8220/21/8/2647 |
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