Prior information-based ellipse fitting optimization method for optical navigation with a short-arc horizon

Optical navigation is a key technology in deep-space exploration. Previous studies have struggled to accurately calculate the celestial model in short-arc horizon scenes, greatly reducing navigation accuracy. To address this issue, this article proposes a high-precision short-arc fitting optimizatio...

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Bibliographic Details
Published in:Engineering optimization 2024-11, Vol.56 (11), p.1880-1896
Main Authors: Qiang, Qichang, Lin, Baojun, Liu, Yingchun, Lin, Xia
Format: Article
Language:English
Subjects:
adaptive trust region
Algorithms
autonomous navigation
Deep space
Elliptic fitting
Navigation
optical navigation
Parameter estimation
Shape optimization
Space exploration
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Summary:Optical navigation is a key technology in deep-space exploration. Previous studies have struggled to accurately calculate the celestial model in short-arc horizon scenes, greatly reducing navigation accuracy. To address this issue, this article proposes a high-precision short-arc fitting optimization algorithm based on prior shape information. The suggested technique is a two-step estimation process that uses minimal horizon features to solve the optimal model parameters iteratively. First, the random sample consensus (RANSAC) algorithm is applied to compute the prior shape information of celestial bodies and suppress the interference of outliers. Then, an improved ellipse optimization equation is designed to integrate the shape information while ensuring efficiency. Finally, to perform precise short-arc fitting, the model parameters are iteratively optimized using an adaptive trust region. Numerous comparison tests demonstrate that the proposed approach achieves rapid convergence in three steps and requires approximately 15-20% less minimum arc length than Christian's method.
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2023.2285898