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    This study introduces an adaptive dynamic adjustment model for star identification thresholds in star trackers. This improves identification efficiency by over 46% while maintaining a 100% success rate.

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    Area of Science:

    • Aerospace Engineering
    • Navigation Systems
    • Computer Vision

    Background:

    • Star trackers are crucial for spacecraft navigation, relying on star image identification matching thresholds.
    • Current fixed thresholds limit identification efficiency and success rates in terrestrial star trackers.
    • Inappropriate matching thresholds hinder redundant matching and overall performance.

    Purpose of the Study:

    • To address the limitations of fixed matching thresholds in terrestrial star trackers.
    • To develop an adaptive dynamic adjustment model for star identification thresholds.
    • To enhance both the identification efficiency and success rate of star trackers.

    Main Methods:

    • Developed an adaptive dynamic adjustment star identification threshold model.
    • The model dynamically estimates thresholds based on varying observation angular distances and attitudes.
    • Incorporated analysis of observation star angular distance errors.

    Main Results:

    • Numerical simulations showed over 46.54% improvement in identification efficiency.
    • Night sky experiments demonstrated a 22.61% increase in identification efficiency.
    • The identification success rate was consistently maintained at 100% in all tests.

    Conclusions:

    • The proposed adaptive model significantly enhances star tracker identification efficiency.
    • The model ensures high identification success rates, crucial for reliable navigation.
    • This approach offers a superior alternative to traditional fixed threshold methods.