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Star Identification Based on Multilayer Voting Algorithm for Star Sensors.

Meiying Liu1,2, Xin Wei1,2, Desheng Wen1

  • 1Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi'an 710119, China.

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Summary
This summary is machine-generated.

This paper introduces a new multilayer voting algorithm for autonomous star identification in spacecraft attitude determination. This method enhances robustness against noise and false stars, offering faster identification speeds.

Keywords:
singular values featurestar identificationstar sensortriangle unit voting

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Area of Science:

  • Spacecraft attitude determination
  • Autonomous navigation
  • Star identification algorithms

Background:

  • Accurate spacecraft attitude determination is crucial for mission success.
  • Existing star identification methods face challenges with noise and computational efficiency.
  • Autonomous systems require robust and rapid identification capabilities.

Purpose of the Study:

  • To introduce a novel multilayer voting algorithm for autonomous star identification.
  • To enhance the robustness and speed of star identification for spacecraft.
  • To improve the accuracy of attitude determination systems.

Main Methods:

  • A two-process approach: initial match and verification.
  • Utilizing a triangle voting scheme with singular values for feature extraction.
  • Employing singular value decomposition (SVD) for efficient candidate searching.
  • Implementing a verification step to eliminate false positives.

Main Results:

  • The multilayer voting algorithm demonstrates superior robustness to position noise, magnitude noise, and false stars compared to three other algorithms.
  • Achieves significantly faster identification speeds than geometric voting and optimized grid algorithms.
  • While requiring more memory, the algorithm shows improved overall performance.

Conclusions:

  • The multilayer voting algorithm offers a robust and efficient solution for autonomous star identification.
  • It significantly improves spacecraft attitude determination accuracy and reliability.
  • The method presents a promising advancement in autonomous navigation technologies.