时域检索与频域扰动双域联合增强的船舶轨迹预测OA

Vessel trajectory forecasting is of significant importance for ensuring maritime safety,optimizing traffic management,and achieving intelligent decision-making.Existing methods are often limited by insufficient data diversity and inadequate use of historical information,making them difficult to deal with complex and dynamic marine environments.To address this issue,a dual-domain joint augmentation method via temporal retrieval and frequency perturbation for vessel trajectory forecasting was proposed.By expanding sample diversity through frequency perturbation and enhancing the model's ability to represent long-term dependencies by drawing on similar historical trajectory sequences,the proposed method significantly improves the generalization and robustness of the prediction model while maintaining high accuracy.Experimental results on multiple real-world vessel trajectory datasets show that the proposed method outperforms the mainstream time-series forecasting models,including LSTM,BiLSTM,DLinear,Transformer,Informer,and iTransformer,in terms of prediction accuracy and stability,verifying the effectiveness and superiority of this method in vessel trajectory forecasting tasks.