Dynamic Positional Attention Fusion (DPAF): Adaptive Encoding and Weighted Attention for Ship motion attitude Prediction

Enhanced accuracy and long-term predictions of ship motion during sea operations can effectively mitigate safety risks associated with aircraft takeoff and landing on board.This paper proposes a transformer-based ship motion attitude prediction model.Our work leverages a novel self-attention mechanism with adaptive position encoding and learnable attention weights to improve long-term prediction accuracy. Furthermore, we also incorporate a pretraining phase using a random masking strategy to enhancing the model's training capability and reducing prediction phase duration.The proposed model is evaluated using data from a ship undergoing constant speed and Z-word motion to predict the roll and pitch angles of the ship. The model is compared with ARMA, EMD-ARMA, LSTM, Bi-LSTM and traditional Transformer models. The experimental results demonstrate that the proposed method outperforms these models in multi-step prediction scenarios.