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A Robust Local Magnitude Fitting Method for Star Identification
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AI
DOI：10.1109/JSEN.2024.3487580.png)
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Abstract
En 中文
Star identification is the most important part of satellite attitude determination. Existing star image identification algorithms show lower robustness with an increase in the number of stars. This study proposes a method for star identification based on local magnitude fitting. First, the similarity of neighboring star images is used for denoising. Then, the Gaussian distribution is used to determine the star point range and calculate the real grayscale cumulative value (RGCV). Finally, the star magnitude fitting range is obtained using the star tracker parameters and the fitting parameters between the RGCV and the star magnitude are determined. This method is used to optimize the rotation invariant additive vector sequence algorithm in this article. The results show that this method can reduce the storage capacity by 96%, enhance the efficiency of the algorithm and achieve a recognition rate of above 98% in real-situations. Furthermore, this method can also be applied to other star identification algorithms.
Keywords:
Stars
Feature extraction
Navigation
Satellites
Robustness
Noise
Sensors
Remote sensing
Classification algorithms
Position measurement
Attitude determination
magnitude
relative installation
star identification
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W