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High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm.

Jianming Zhang1, Junxiang Lian1, Zhaoxiang Yi1

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Improving star identification for spacecraft attitude determination is crucial for gravitational wave detection. K-Nearest Neighbours (KNN) generated a superior guide star catalogue (GSC), enhancing storage, uniformity, and completeness for high-accuracy satellite applications.

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Area of Science:

  • Spacecraft attitude determination
  • Gravitational wave detection
  • Interferometric measurements

Background:

  • Laser links between three satellites are used for gravitational wave detection.
  • Star sensors (SSR) provide spacecraft attitude, crucial for laser link establishment.
  • Improving SSR attitude measurement enhances laser linking efficiency.

Purpose of the Study:

  • To enhance the Guide Star Catalogue (GSC) for improved spacecraft attitude determination.
  • To optimize GSC storage, completeness, and uniformity.
  • To increase the efficiency of establishing laser links for scientific experiments.

Main Methods:

  • Discussed the relationship between star numbers in the field of view (FOV) and SSR noise equivalent angle (NEA).
  • Applied constraints based on number of stars (NOS), brightness, and FOV size to select stars from the SAO GSC.
  • Assessed GSCs generated by various algorithms including Decision Trees, KNN, SVM, MFM, GB, NN, RF, and SGD.

Main Results:

  • The K-Nearest Neighbours (KNN) method generated a GSC superior in storage, uniformity, and completeness compared to other methods.
  • The study identified a relationship between SSR's FOV, NEA, and the number and brightness of stars within the FOV.
  • KNN-generated GSCs demonstrated suitability for high-accuracy applications.

Conclusions:

  • The KNN method provides an optimal approach for generating improved Guide Star Catalogues.
  • Enhanced GSCs are essential for high-accuracy spacecraft applications like gravitational wave detection.
  • Optimized star identification directly contributes to the efficiency of space-based scientific missions.