A multi-task deep learning network for robust and high accuracy star sensor image centroiding
Year: 2026
Type: article
Abstract: Star sensors require reliable onboard centroid estimation despite binary-star interference and non-Gaussian distortions. Conventional algorithms lack robustness, while deep learning models often sacrifice accuracy or computational efficiency for real-time operation. This study proposes a lightweight multi-task CNN based on the HorNet baseline for onboard deployment. It simultaneously distinguishes single-star from binary-star targets and estimates sub-pixel centroids in a single forward pass. Two improvement plug-in modules—differentiable Gaussian convolution for noise suppression and coordinate attention for spatial enhancement—add minimal overhead, enhancing the centroid estimation accuracy for star spots of various morphologies. A Pareto-optimal loss function balances classification and regression without manual tuning. Experiments on a dataset (including ideal Gaussian, real non-Gaussian profiles, and binary-star interference) show that the proposed model achieves 100.0% binary-star detection rate, substantially outperforming conventional algorithms (≈ 53.7% accuracy). Under simulated non-Gaussian conditions, it attains a centroid error of ≈ 0.017 pixels, outperforming the conventional centroid method (≈ 0.16 pixels) and Gaussian fitting (≈ 0.12 pixels). The model remains compact, efficient, and easy to train, offering a structurally efficient solution with demonstrated potential for high-precision, real-time processing stellar centroid estimation on current GPU platforms, demonstrating potential for future onboard deployment.
Source: Scientific Reports
Authors: Jian Guan, Xiaodi Meng, Yanpeng Wu, Xiaofeng Li, Yan Xiong, Jingya Qi
Institutions: Xidian University, Beijing Institute of Control Engineering
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7210274749
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