Reliable Star Pattern Identification Technique by Using Neural Networks

Abstract

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.