Year: 2023
Type: article
Abstract: This article presents arotation-search-basedapproach 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 thebranch-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 leveragesstereographic projectionand theR-treedata structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise.
Authors: Chee-Kheng Chng, Alvaro Parra Bustos, Benjamin McCarthy, Tat-Jun Chin
Institution: The University of Adelaide
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4379034047