A Review of Pattern Matching Methods in Star Trackers

The main problem in the star trackers is when it is in the lost-in-space mode that no attitude information is known a priori. In this mode, using algorithms should be robust against false stars in acquired images and be able to estimate the accurate attitude of theplatform in proper time. Processing algorithms consist of three main parts: star centroid estimation, pattern recognition and star identification, and attitude determination. In this study, the different stellar patterns are described that are used in matching and star identification process. For this, the position of stars within sensor’s field of view is calculated in the image coordinate system and the stellar patterns are generated using these positions. Then, the guide stars are selected from the well-known astronomical catalogue which are used to facilitate pattern generation and recognition based on using algorithms. If the image stellar pattern matches a unique one built up by guide stars, theidentification is accomplished. In this study, three common pattern recognition methods such as the triangle, planar triangle and spherical triangle methods were reviewed and were compared based on the required time, efficiency, the success rate, etc. As a result, the performance and the success rate of the planar triangle method is higher than twoother methods without a high computational burden and complexity.