In this article, we consider the problem of extracting star spots for traditional star sensors under highly dynamic conditions. Under this setting, it is difficult to effectively extract star spots with a low signal-to-noise ratio (SNR), resulting in a failure to estimate the attitude of star sensors. We propose a method named optimal directional connected component (ODCC) for this task. According to the dynamic characteristics of star sensors, we propose an image enhancement method that can adaptively estimate the directions of star spots and integrate the star image so that the SNR of the star spots is increased. This aids in searching for spots with a low SNR. According to the imaging characteristics of a star, we model the imaging region of a star as a rectangle and then creatively cast the problem of extracting spots into a problem of estimating the endpoints of the rectangle. A maximum likelihood estimation method based on the Gaussian mixture model is proposed to determine the region of the star spot. Experiments demonstrate the extraction capability and accuracy of the method. In conclusion, this method has a promising application value for improving the dynamic performance of traditional star sensors.

中文翻译：

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ODCC：一种星敏感器的动态星斑提取方法


在本文中，我们考虑在高动态条件下提取传统星敏感器的星点问题。在此设置下，难以有效提取低信噪比（SNR）的星点，导致无法估计星敏感器的姿态。我们为此任务提出了一种名为最佳定向连通分量（ODCC）的方法。根据星敏感器的动态特性，提出一种图像增强方法，能够自适应地估计星斑方向并对星图像进行积分，从而提高星斑的信噪比。这有助于搜索低信噪比的点。根据恒星的成像特点，我们将恒星的成像区域建模为一个矩形，然后创造性地将光点提取问题转化为估计矩形端点的问题。提出了一种基于高斯混合模型的最大似然估计方法来确定星斑区域。实验证明了该方法的提取能力和准确性。综上所述，该方法对于提高传统星敏感器的动态性能具有良好的应用价值。