The required precision for attitude determination in spacecraft is increasing, providing a need for more accurate attitude determination sensors. The star sensor or star tracker provides unmatched arc-second precision and with the rise of micro satellites these sensors are becoming smaller, faster and more efficient. The most critical component in the star sensor system is the lost-in-space star identification algorithm which identifies stars in a scene without a priori attitude information. In this paper, we present an efficient lost-in-space star identification algorithm using a neural network and a robust and novel feature extraction method. Since a neural network implicitly stores the patterns associated with a guide star, a database lookup is eliminated from the matching process. The search time is therefore not influenced by the number of patterns stored in the network, making it constant (O(1)). This search time is unrivalled by other star identification algorithms. The presented algorithm provides excellent performance in a simple and lightweight design, making neural networks the preferred choice for star identification algorithms.

中文翻译：

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使用神经网络进行有效的恒星识别。

航天器中姿态确定所需的精度不断提高，这就需要更精确的姿态确定传感器。恒星传感器或恒星跟踪仪可提供无与伦比的弧秒精度，随着微型卫星的兴起，这些传感器变得越来越小，越来越快，效率越来越高。恒星传感器系统中最关键的组件是空间丢失恒星识别算法，该算法无需先验的姿态信息即可识别场景中的恒星。在本文中，我们提出了一种使用神经网络和健壮且新颖的特征提取方法的有效的空间丢失恒星识别算法。由于神经网络隐式存储与引导星关联的模式，因此从匹配过程中消除了数据库查找。因此，搜索时间不受网络中存储的模式数量的影响，从而使其恒定（O（1））。该搜索时间是其他恒星识别算法无法比拟的。提出的算法在简单轻巧的设计中提供了出色的性能，使神经网络成为恒星识别算法的首选。