Intel Corporation, Intel R&D Ireland Ltd, Collinstown, Collinstown Industrial Park, Co. Kildare, W23 CX68, Ireland.
European Space Agency/ESTEC, Keplerlaan 1 2201AZ, Noordwijk, The Netherlands.
Sensors (Basel). 2020 Jun 30;20(13):3684. doi: 10.3390/s20133684.
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.
航天器的姿态确定精度要求不断提高，这就需要更精确的姿态确定传感器。星敏感器或星跟踪器提供无与伦比的角秒级精度，随着微卫星的兴起，这些传感器变得更小、更快、更高效。星敏感器系统中最关键的组件是在没有先验姿态信息的情况下识别场景中恒星的“迷失太空”恒星识别算法。在本文中，我们提出了一种使用神经网络和一种稳健新颖的特征提取方法的高效“迷失太空”恒星识别算法。由于神经网络隐式地存储与导星相关的模式，因此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，而是保持不变（O(1)）。这种搜索时间是其他恒星识别算法无法比拟的。所提出的算法在简单而轻量级的设计中提供了出色的性能，因此神经网络是恒星识别算法的首选。
