EclipseNETs: Learning Irregular Small Celestial Body Silhouettes
This addresses a critical challenge in spacecraft operations for navigation and orbit determination, offering a novel method that is incremental in improving efficiency and adaptability.
The paper tackles the problem of predicting eclipse events around irregular small celestial bodies for spacecraft navigation by introducing a neural implicit representation approach, achieving accuracy comparable to traditional ray-tracing with orders of magnitude faster performance on four test bodies.
Accurately predicting eclipse events around irregular small bodies is crucial for spacecraft navigation, orbit determination, and spacecraft systems management. This paper introduces a novel approach leveraging neural implicit representations to model eclipse conditions efficiently and reliably. We propose neural network architectures that capture the complex silhouettes of asteroids and comets with high precision. Tested on four well-characterized bodies - Bennu, Itokawa, 67P/Churyumov-Gerasimenko, and Eros - our method achieves accuracy comparable to traditional ray-tracing techniques while offering orders of magnitude faster performance. Additionally, we develop an indirect learning framework that trains these models directly from sparse trajectory data using Neural Ordinary Differential Equations, removing the requirement to have prior knowledge of an accurate shape model. This approach allows for the continuous refinement of eclipse predictions, progressively reducing errors and improving accuracy as new trajectory data is incorporated.