Empirical Analysis of Common Subgraph Isomorphism Approaches to the Lost-in-Space Star Identification Problem

Abstract: The process of identifying stars is integral toward stellar based orientation determination in spacecraft. Star identification involves matching points in an image of the sky with stars in an astronomical catalog. A unified framework for identification was created and used to analyze six variations of methods based on their approach to star set identification, obtaining a single image to catalog star set match, and uniquely mapping each star in a image star set to a catalog star set. Each method was presented an artificial image, and aspects that were interchangeable among each process were normalized. Given an image with false stars, the Pyramid method has the highest average accuracy and is the fastest of the six. Given an image where each star's true position is distributed randomly (Gaussian noise), the Spherical Triangle method's accuracy is the least sensitive.