Lost-in-space star identification algorithm based on Hausdorff distance with two approaches: Pivot star and celestial sphere segmentation

One of the best attitude sensors for space applications is the star sensor. This sensor determines the attitude using stars in the field of view. One of the main advantages of this sensor is attitude initialization using lost-in-space star identification algorithms. This paper presents a new lost-in-space star identification algorithm based on Hausdorff distance. Using Hausdorff distance, two different identification algorithms have been proposed, and their results have been compared. The first approach is designed based on using the pivot star, and the second approach uses the segmentation of the celestial sphere. The performance of these two approaches has been investigated using the simulation of 200 random directions of the star sensor in different magnitudes. The results show the approach of using the pivot star has better performance, and its identification rate is 93.5% at the magnitude of 6. Also, the identification time of the Hausdorff algorithm has been compared with the pyramid algorithm and some geometric algorithms. The results show that the Hausdorff identification algorithm has the lowest identification time which makes it suitable for real-time applications.