Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
This work addresses data quality issues for satellite operators and space situational awareness, providing insights into decay processes, but it is incremental as it applies existing methods to a new dataset.
This study analyzed the accuracy of publicly available ephemeris data for Starlink satellites, comparing it to high-precision numerical propagation and identifying discrepancies, with position RMSE of about 300 m for non-deorbiting and 600 m for deorbiting satellites. It used Physics-Informed Machine Learning to extract satellite quantities like non-conservative forces during decay, proposing a data-driven model for satellite decay in mega-constellations.
In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Starlink. The goal of this work is twofold: (i) compare and analyze the quality of the data against high-precision numerical propagation. (ii) Leverage Physics-Informed Machine Learning to extract relevant satellite quantities, such as non-conservative forces, during the decay process. By analyzing two months of real orbital data for approximately 1500 Starlink satellites, we identify discrepancies between high precision numerical algorithms and the published ephemerides, recognizing the use of simplified dynamics at fixed thresholds, planned maneuvers, and limitations in uncertainty propagations. Furthermore, we compare data obtained from multiple sources to track and analyze deorbiting satellites over the same period. Empirically, we extract the acceleration profile of satellites during deorbiting and provide insights relating to the effects of non-conservative forces during reentry. For non-deorbiting satellites, the position Root Mean Square Error (RMSE) was approximately 300 m, while for deorbiting satellites it increased to about 600 m. Through this in-depth analysis, we highlight potential limitations in publicly available data for accurate and robust Space Situational Awareness (SSA), and importantly, we propose a data-driven model of satellite decay in mega-constellations.