Paper
5 November 2020 Triangle star identification algorithm based on hash map
Author Affiliations +
Proceedings Volume 11567, AOPC 2020: Optical Sensing and Imaging Technology; 115673U (2020) https://doi.org/10.1117/12.2580248
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China
Abstract
Triangle star identification algorithm is the most widely used and most mature star pattern recognition algorithms. When the number of guide star is relatively huge and the capacity of guide star catalog is relatively large, with the result that the complexity of triangle star identification algorithm increases, the time of star pattern recognition becomes longer, and even the storage space occupied by the algorithm becomes larger. So it is difficult to realize the rapid and effective star identification in star map. In order to improve the efficiency of star identification algorithm and shorten the time of star recognition, it is proposed that a star identification algorithm used on the data structure of hash map and based on the triangle algorithm. The first thing is to make the guide star catalog. Then, all the angular distance values d ijm (0 < i < jN) of the brightest N observed stars in an observed star image and their corresponding star angular distance sets are calculated, and the triangle features, namely angular distance values, are stored in the hash map. In this algorithm, each triangle feature is mapped to an integer, and the hash map of all triangle features is set to reduce the computational complexity of triangle pattern matching, decrease the number of star angular distance matching, and greatly shorten the time of star image recognition. Simulation results show that the star image recognition algorithm based on hash map has better computational complexity and efficiency of performance than traditional triangle algorithm.
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Yujing Tang, Xingshu Wang, Dongkai Dai, Wenfeng Tan, and Youlin Wu "Triangle star identification algorithm based on hash map", Proc. SPIE 11567, AOPC 2020: Optical Sensing and Imaging Technology, 115673U (5 November 2020); https://doi.org/10.1117/12.2580248
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