Abstract
In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.
Highlights
The identification rate of the triangle algorithm drops because the algorithm uses the angular distance between stars for matching, and the accuracy of star point extraction under dynamic conditions has a great impact on the result
An end-to-end star identification (star-ID) algorithm based on neural networks for smearing star images is proposed in this paper
The networks can efficiently realize the main star-ID by extracting different features and focusing on learning the relative position information between stars
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reference [16] proposes a star-ID based on rolling shutter compensation robust to angular rates They did not discuss extreme situations under high dynamics, which is the trend of agile satellite development. Phase information of smearing is used for Wiener filtering [21], but the noise is not considered in the model These algorithms have not discussed the robustness of star-ID under dynamic conditions. Require feature preprocessing to identify by artificially constructing patterns, which is difficult for smearing stars These algorithms do not discuss the specific issues under motion conditions or consider the characteristics of dynamic stars.
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