A Star Identification Algorithm Based on Recommended Radial Pattern

Abstract

Star identification is a key technology in the research of star sensors. As a classical star pattern for identification, the radial pattern of a chosen star represents the geometric distribution of neighboring stars. Previous researches usually combine the rotation-invariant radial pattern with other less reliable patterns like cyclic pattern to identify the chosen star. Seldom studies use the radial pattern for direct identification as it is sensitive to noise and inefficient to eliminate spurious matches. To solve the problem, a novel star identification algorithm based on the recommended radial pattern was developed. The algorithm introduced the Apriori algorithm to mine the association rules between a star and neighboring stars, recommend the companion stars, and generate the recommended radial pattern. Based on the association rules, the algorithm integrated the matching results of a star and recommended companion stars to identify the chosen star. The simulation test and night sky image test both show that the proposed algorithm is robust to position noise, brightness noise, and false stars. The novel matching strategy is not limited to the radial pattern algorithms, it provides a new idea for all the star pattern algorithms.