Satellite-to-Vessel Line-of-Sight Probability Prediction Model for Maritime Scenarios

Traditional satellite-to-vessel (S2V) models struggle to handle wave movement and random obstacles in complex maritime environments. These models are designed for urban scenarios and fail to account for the unique dynamics of sea surface fluctuations, obstacle distribution, and satellite posture, limiting their applicability in maritime communications. We propose a novel empirical model that integrates multiple maritime environmental factors to overcome these challenges. A realistic virtual sea surface scenario is constructed by extracting oceanic geometric features and statistically modelling waves and obstacles. A large volume of line-of-sight (LoS) data is obtained through ray tracing (RT) simulations. A multi-input graph convolutional network (GCN) is trained for parameter estimation, and a new complexity parameter is introduced to characterize environmental impact. Simulation results demonstrate that the proposed method achieves higher prediction accuracy in maritime communication scenarios and significantly outperforms baseline models. Even with simplified inputs, the model performs well in urban scenarios, demonstrating strong generalization and adaptability.