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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-04, Vol.21 (8), p.2647
Main Authors: Zhang, Jianming, Lian, Junxiang, Yi, Zhaoxiang, Yang, Shuwang, Shan, Ying
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Astronomical catalogs
Classification
Completeness
Efficiency
Gravitational waves
guide star catalogue
Identification
Lasers
Machine learning
Neural networks
Sensors
Spacecraft docking
star sensor
Stars & galaxies
Support vector machines
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21082647