SMAPNet: A Hybrid Ship Motion Attitude Prediction Network Integrating Incremental Decomposition

An accurate prediction of the short-term motion attitude of ships is essential for navigation safety and offshore operations. However, conventional time series prediction models have constraints in handling time-varying dynamics and adapting to diverse sea states. Therefore, Ship Motion Attitude Prediction Network (SMAPNet) based on Non-Symmetric Tri-Cube Kernel Trend Filter (NTKTF) is proposed in the present paper. SMAPNet decomposes temporal signals using the Feature Extraction Block (FEB), fuses local and global features through Feature Refinement Block (FRB), and integrates Bidirectional Long Short-Term Memory Network (Bi-LSTM) with a self-attention mechanism, Feature Prediction Block (FPB), for short-term prediction within 1 to 5 s. In this experiment, field-measured data from the ship XIN HONG ZHUAN were employed to construct online prediction scenarios, and a systematic evaluation was conducted from three perspectives: local prediction accuracy, evaluation metric, and error distribution. The findings indicate that SMAPNet exhibits improved adaptability and prediction accuracy in predicting ship motion attitudes under different sea states. Specifically, in the single-step prediction of roll and pitch under sea states 3 and 4, the mean square errors (MSE) of SMAPNet are reduced by 10.45%, 6.96% and 14.60%, 2.77% respectively compared with the superior candidate model.