Learning Satellite Pattern-of-Life Identification: A Diffusion-Based Approach

As Earth's orbital satellite population grows exponentially, effective space situational awareness becomes critical for collision prevention and sustainable operations. Current approaches to monitor satellite behaviors rely on expert knowledge and rule-based systems that scale poorly. Among essential monitoring tasks, satellite pattern-of-life (PoL) identification—analyzing behaviors like station-keeping maneuvers and drift operations—remains underdeveloped due to aerospace system complexity, operational variability, and inconsistent ephemerides sources. We propose a novel generative approach for satellite PoL identification that significantly eliminates the dependence on expert knowledge. The proposed approach leverages orbital elements and positional data to enable automatic pattern discovery directly from observations. Our implementation uses a diffusion model framework for end-to-end identification without manual refinement or domain expertise. The architecture combines a multivariate time-series encoder to capture hidden representations of satellite positional data with a conditional denoising process to generate accurate PoL classifications. Through experiments across diverse real-world satellite operational scenarios, our approach demonstrates superior identification quality and robustness across varying data quality characteristics. A case study using actual satellite data confirms the approach's transformative potential for operational behavior pattern identification, enhanced tracking, and space situational awareness.