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Star-Fusion transformer architecture improves celestial orientation for spacecraft

By PulseAugur Editorial · [3 sources] ·

Researchers have developed Star-Fusion, a novel multi-modal transformer architecture designed for precise celestial orientation determination in spacecraft navigation. This approach reframes the problem as a discrete topological classification task, utilizing spherical K-Means clustering to manage the complexities of the celestial sphere's non-Euclidean topology. The architecture integrates a SwinV2-Tiny transformer for feature extraction, a convolutional heatmap branch for spatial grounding, and an MLP for geometric anchoring. Experimental results show Star-Fusion achieving 93.4% Top-1 accuracy with an inference latency of 18.4 ms, making it suitable for real-time onboard satellite applications. AI

IMPACT This new architecture could improve the accuracy and efficiency of autonomous spacecraft navigation systems.

RANK_REASON This is a research paper describing a new model architecture for a specific technical problem.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Star-Fusion transformer architecture improves celestial orientation for spacecraft

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Star-Fusion: A Multi-modal Transformer Architecture for Discrete Celestial Orientation via Spherical Topology

    Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional "Lost-in-Space" (LIS) algorithms often suffer from high computational overhead and sensitivity to sensor-induced noise. While deep learning has emerged as a p…

  2. arXiv cs.CV TIER_1 English(EN) · May Hammad, Menatallh Hammad ·

    Star-Fusion: A Multi-modal Transformer Architecture for Discrete Celestial Orientation via Spherical Topology

    arXiv:2604.26582v1 Announce Type: new Abstract: Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional "Lost-in-Space" (LIS) algorithms often suffer from high computational overhead and sensitivity to sensor-induc…

  3. arXiv cs.CV TIER_1 English(EN) · Menatallh Hammad ·

    Star-Fusion: A Multi-modal Transformer Architecture for Discrete Celestial Orientation via Spherical Topology

    Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional "Lost-in-Space" (LIS) algorithms often suffer from high computational overhead and sensitivity to sensor-induced noise. While deep learning has emerged as a p…

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