Accurate sea state estimation is of great significance to marine engineering safety and disaster prevention and mitigation. This paper proposes a new sea state estimation architecture integrating multi-scale 1D-CNN, transformer encoder, and the MC-DropKey mechanism. First, the front-end multi-scale 1D-CNN is used in sequence modeling to extract high-quality local features. Then, the back-end transformer encoder is used to capt...

High-fidelity ship dynamics models are essential for the reliable operation of maritime autonomous systems. However, existing Markov-based maneuvering models and purely data-driven predictors struggle to capture hydrodynamic memory and degrade under non-ideal sensing. To address these challenges, this paper proposes a novel approach for robust ship motion prediction, the Non-Markovian Memory-Augmented Environment-Perceived and...