A Star-Identification Algorithm Based on Global Multi-Triangle Voting

Abstract

A star-identification algorithm aimed at identifying imaged stars in a “lost in space” scene, named the global multi-triangle voting algorithm (GMTV), is presented in this paper. There are two core parts included in the proposed algorithm: in the initial match part, triangle feature units are treated as vote units to find the initial match relationship via matching vote units and counting the vote number of each catalog star. During this step, the principal component analysis (PCA) method is implemented to reduce feature dimensions, and a two-dimension lookup table and fuzzy match strategy are utilized to promote database searching efficiency and noise tolerance. After acquiring the initial match results, a verification part is implemented to filter potential errors from initial candidates by the largest cluster method and output the final identification results. The proposed algorithm achieves a 98.6% identification rate with 2.0 pixels position noise and exhibits more robustness to position noise, magnitude noise, and false stars of different levels than the two reference algorithms used in simulations. In addition, our algorithm’s real-time performance is better than reference algorithms, but it requires a larger database.