Physics-Informed Neural Networks for Satellite State Estimation

arXiv:2403.19736v1 Announce Type: cross
Abstract: The Space Domain Awareness (SDA) community routinely tracks satellites in orbit by fitting an orbital state to observations made by the Space Surveillance Network (SSN). In order to fit such orbits, an accurate model of the forces that are acting on the satellite is required. Over the past several decades, high-quality, physics-based models have been developed for satellite state estimation and propagation. These models are exceedingly good at estimating and propagating orbital states for non-maneuvering …

abstract acting arxiv astro-ph.im community cs.ai cs.lg domain in orbit network networks neural networks physics physics-informed satellite satellites space state surveillance type