Federated-Learning-Based Strategy for Enhancing Orbit Prediction of Satellites

As the primary public source of satellite trajectory data, the Two-Line Element (TLE) dataset offers fundamental orbital parameters for space missions. However, for satellites with poor data quality, traditional neural network models often underperform, hindering accurate orbit predictions and meeting demands in satellite operation and space mission planning. To address this, a federated-learning-based trajectory prediction enhancement strategy is proposed. Satellites with low training efficiency and similar orbits are grouped for collaborative learning. Each satellite uses a Convolutional Neural Network (CNN) model to extract features from historical prediction error data. The server optimizes the global model through the Federated Averaging algorithm, learning more orbital patterns and enhancing accuracy. Experimental results confirm the method’s effectiveness, with a marked increase in prediction accuracy compared to traditional methods, validating federated learning’s advantage. Moreover, the combination of federated learning with basic neural network models like the Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU) is explored. The results indicate that integrating federated learning can greatly enhance satellite prediction, opening new possibilities for future orbital prediction and space technology development.