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Description
Increasing solar activity significantly alters the thermospheric density, causing unpredictable orbital decay for satellites in Low Earth Orbit (LEO). This study proposes an intelligent forecasting framework that utilizes Machine Learning to predict real-time changes in atmospheric drag and their subsequent impact on collision probabilities. The primary objective is to develop an autonomous system capable of adjusting satellite maneuver windows based on predicted space weather-induced atmospheric expansion.
The methodology leverages public datasets, including historical Two-Line Element (TLE) orbital data and solar indices (e.g., F10.7 flux and Kp indices). We employ a Long Short-Term Memory (LSTM) network to model the time-series relationship between solar activity and atmospheric drag at various orbital altitudes. By integrating this density forecast into a collision avoidance algorithm, the system calculates the "maneuver necessity" for active satellites. This approach eliminates the reliance on ground-based, high-latency atmospheric models.
The proposed framework is expected to improve the precision of orbit-decay predictions by 25% compared to standard models, enabling more efficient fuel usage for station-keeping. The significance of this work is its direct applicability to the current LEO satellite congestion crisis. By providing operators with an automated tool for anticipating drag-driven orbital changes, this research provides a scalable solution for maintaining safety in an increasingly crowded orbital environment, ultimately supporting the long-term sustainability of space operations.