Machine Learning Approach for Accurate and Robust Satellite Tracking in Optical Space-to-Ground Communication using Time-Series Prediction for LEO Satellites

Dies ist die aktuellste Version dieses Eintrags.

Kurzfassung

As space becomes increasingly congested with Resident Space Objects (RSOs) in Low Earth Orbit (LEO), improving the accuracy of orbit prediction is crucial for ensuring operational reliability, particularly for satellite tracking in optical communication and Telemetry, Tracking, and Command (TTC) operations. This work focuses on refining orbit prediction by leveraging machine learning techniques to enhance tracking capabilities. Traditional orbit approximation relies on the Simplified Perturbations model (SGP4), which calculates a satellite’s position and velocity by considering various perturbations, such as Earth’s gravitational irregularities and atmospheric drag, using an empirical model for efficient orbit determination. However, this approach is prone to errors, as it simplifies complex orbital dynamics. To address this limitation, this paper explores the potential of machine learning algorithms to analyze time-dependent data, with a particular focus on systematic deviations from SGP4 predictions that are inherently captured in historical orbit information. To achieve this, we create a set of data consisting of time-series satellite position data sets of LEO Objects from past Two-Line Elements (TLEs) as well as orbital messages derived form Global Navigation Satellite System (GNSS) observations and Laser Ranging Data. These data sets are used to train various machine learning models specialized in time series data, such as Long-Short-Term Memory (LSTM) networks to evaluate their potential for improving the robustness and accuracy of orbit forecasting. Finally, the performance of the machine learning model is evaluated by comparing its predictions with those from the traditional SGP4 model. In the future, we will be assessing prediction accuracy and analyze Radial, In-Track, and Cross-Track (RIC) errors to ensure the new model’s effectiveness using measurements from Optical Ground Stations at DLR.

Verfügbare Versionen dieses Eintrags

• Machine Learning Approach for Accurate and Robust Satellite Tracking in Optical Space-to-Ground Communication using Time-Series Prediction for LEO Satellites. (deposited 15 Dez 2025 11:10) [Gegenwärtig angezeigt]