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Efficient Star Identification Using a Neural Network.

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This study introduces an efficient lost-in-space star identification algorithm using neural networks. This novel approach offers constant O(1) search time, outperforming existing methods for spacecraft attitude determination.

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Area of Science:

  • Spacecraft attitude determination
  • Sensor technology
  • Artificial intelligence in aerospace

Background:

  • Increasing precision requirements for spacecraft attitude determination necessitate advanced sensors.
  • Star trackers offer arc-second precision and are becoming smaller, faster, and more efficient for micro-satellites.
  • Lost-in-space star identification algorithms are critical for autonomous attitude determination without prior information.

Purpose of the Study:

  • To present an efficient lost-in-space star identification algorithm for spacecraft.
  • To leverage neural networks and a novel feature extraction method for improved performance.
  • To achieve constant O(1) search time for star identification.

Main Methods:

  • Development of a novel feature extraction method.
  • Implementation of a neural network for implicit pattern storage.
  • Elimination of database lookup in the star matching process.

Main Results:

  • Achieved constant O(1) search time, independent of stored patterns.
  • Demonstrated unrivalled search speed compared to other star identification algorithms.
  • The algorithm exhibits excellent performance in a simple and lightweight design.

Conclusions:

  • Neural networks are a preferred choice for star identification algorithms due to their efficiency and performance.
  • The proposed algorithm meets the growing demand for accurate and fast attitude determination sensors.
  • The lightweight design is suitable for micro-satellite applications.