An approach to systematically structuring star patterns and an associated identification technique using a neural network and rule-based expert system is addressed. In stellar-inertial navigation systems based upon modern star trackers, star pattern identification has been characterized primarily as a database search problem. The proposed algorithm herein provides a methodology on how to efficiently construct a mission catalog that reduces the size of original data. Grouping neighboring stars for identification makes it possible to decrease data without noticeable performance degradation. The Radial Basis Function (RBF) well known for generic pattern recognitions is employed as a classifier in the simulation. The neural network-based approach in this study, because of associating the star distribution patterns, turns out to decrease computational time in actual space missions. It also minimizes potential noise effects due to clustering algorithm error and/or CCD pixels. Accuracy and performance of the proposed star identification approach have been examined by simulations using the Bright Star Catalog (BSC) J2000 master catalog.

中文翻译：

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神经网络的可靠星型识别技术

提出了一种使用神经网络和基于规则的专家系统来系统构建星型的方法以及相关的识别技术。在基于现代恒星追踪器的恒星惯性导航系统中，恒星模式识别已被主要描述为数据库搜索问题。本文中提出的算法提供了一种关于如何有效地构造减少原始数据大小的任务目录的方法。通过对相邻恒星进行分组以进行识别，可以减少数据而不会明显降低性能。在通用模式识别中众所周知的径向基函数（RBF）被用作模拟中的分类器。在本研究中，基于神经网络的方法由于关联了恒星分布模式，原来可以减少实际太空飞行中的计算时间。它还将由于聚类算法错误和/或CCD像素引起的潜在噪声影响降至最低。拟议的恒星识别方法的准确性和性能已通过使用“亮星目录”（BSC）J2000主目录的模拟进行了检验。