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arXiv Machine Learning By Jeongbin Sohn, Jina Lee, Max Yen, Dowan Gwon, Jeahn Han, Floyd Hepburn-Dickins, Brian Coltin, Ryan Soussan, Pyojin Kim

SIGNN++: Robust Graph Neural Star Identification on Earth

Read the original on arXiv Machine Learning →

SIGNN++ is a learning-based star identification framework designed for celestial navigation in GNSS‑denied nighttime environments. It builds on the earlier SIGNN graph neural network by adding a realistic noise model, Fourier Transform of angular‑distance edge features, and a RANSAC‑based perspective star alignment step. Experiments show that SIGNN++ achieves higher star identification accuracy and solved rates than SIGNN and other publicly available baselines while maintaining comparable attitude accuracy and practical computation time for continuous operation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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