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An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

Liyan Luo1, Luping Xu2, Hua Zhang3

  • 1School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China. liyanluo@stu.xidian.edu.cn.

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Summary
This summary is machine-generated.

A new autonomous star identification algorithm uses a one-dimensional vector pattern (one_DVP) to improve speed and robustness. This method simplifies star identification by comparing feature vectors, outperforming existing algorithms.

Keywords:
one-dimensional vector patternstar identificationstar patternstar sensor

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Area of Science:

  • Aerospace Engineering
  • Computer Science
  • Astrophysics

Background:

  • Star identification is crucial for autonomous spacecraft navigation.
  • Existing algorithms face challenges in recognition speed and robustness.

Purpose of the Study:

  • To develop an autonomous star identification algorithm that enhances robustness and accelerates recognition speed.
  • To improve the performance of star sensors in space applications.

Main Methods:

  • A novel algorithm based on the one-dimensional vector pattern (one_DVP) is proposed.
  • The algorithm utilizes the spatial geometry of observed stars to form unique feature vectors.
  • Star identification is simplified to comparing these feature vectors, invariant to stellar image rotation.

Main Results:

  • The one_DVP algorithm demonstrates significantly improved recognition accuracy and robustness compared to pyramid, modified grid, and LPT algorithms.
  • Simulation results confirm the algorithm's ability to effectively accelerate star identification.
  • Theoretical analysis and experimental data validate the superior performance of the proposed method.

Conclusions:

  • The proposed one-dimensional vector pattern algorithm offers a robust and efficient solution for autonomous star identification.
  • This algorithm represents a significant advancement for star sensor technology, enhancing navigation reliability.
  • The findings suggest a new standard for high-performance star identification in space missions.