Masked and Clustered Pre-Training for Geosynchronous Satellite Maneuver Detection

Geosynchronous satellite maneuver detection is critical for enhancing space situational awareness and inferring satellite intent. However, traditional methods often require high-quality orbital sequence data and heavily rely on hand-crafted features, limiting their effectiveness in complex real-world environments. While recent neural network-based approaches have shown promise, they are typically trained in scene or task-specific settings, resulting in limited generalization and adaptability. To address these challenges, we propose MC-MD, a pre-training framework that integrates Masked and Clustered learning strategies to improve the robustness and transferability of geosynchronous satellite Maneuver Detection. Specifically, we introduce a masked prediction module that applies both time- and frequency-domain masking to help the model capture temporal dynamics more effectively. Meanwhile, a cluster-based module guides the model to learn discriminative representations of different maneuver patterns through unsupervised clustering, mitigating the negative impact of distribution shifts across scenarios. By combining these two strategies, MC-MD captures diverse maneuver behaviors and enhances cross-scenario detection performance. Extensive experiments on both simulated and real-world datasets demonstrate that MCMD achieves significant performance gains over the strongest baseline, with improvements of 8.54% in Precision and 7.8% in F1-Score. Furthermore, reconstructed trajectories analysis shows that MC-MD more accurately aligns with the ground-truth maneuver sequence, highlighting its effectiveness in satellite maneuver detection tasks.