PROJECT TITLE :
Iterative algorithm for autonomous star identification
An autonomous star identification algorithm is described during this study. This algorithm iteratively determines a catalog star, the distances of that to its neighbors are like those of a given sensor star and its neighboring stars, till a unique match is found. In every iteration, only the catalog stars that receive sufficient votes are utilized in the next steps. We conjointly develop a helpful probabilistic model for performance prediction and analysis. Analysis indicates that spurious star pairings are decreased exponentially after each iteration. The simulation and real sky image results show that the proposed approach is more robust than typical algorithms and needs fewer resources.
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