ROSIA: Rotation-Search-Based Star Identification Algorithm
Abstract
This paper 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 paper, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400x 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. The source code of ROSIA is available at https://github.com/ckchng/ROSIA.
Cite
@article{arxiv.2210.00429,
title = {ROSIA: Rotation-Search-Based Star Identification Algorithm},
author = {Chee-Kheng Chng and Alvaro Parra Bustos and Benjamin McCarthy and Tat-Jun Chin},
journal= {arXiv preprint arXiv:2210.00429},
year = {2023}
}
Comments
21 pages, 16 figures, Accepted to IEEE Transactions on Aerospace and Electronic Systems