This article presents a rotation-search-based approach for addressing the star identification (Star-ID) problem. The proposed algorithm, ROSIA, is a heuristics-free algorithm that seeks the optimal rotation that maximally aligns the input and catalog stars in their respective coordinates. ROSIA searches the rotation space systematically with the branch-and-bound (BnB) method. Crucially affecting the runtime feasibility of ROSIA is the upper bound function that prioritizes the search space. In this article, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400× speed-up compared to an existing formulation. Coupling the bounding function with an efficient evaluation scheme that leverages stereographic projection and the R-tree data structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise.


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    Titel :

    ROSIA: Rotation-Search-Based Star Identification Algorithm


    Beteiligte:


    Erscheinungsdatum :

    2023-10-01


    Format / Umfang :

    5065998 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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