A Prototype-Empowered Kernel-Varying Convolutional Model for Imbalanced Sea State Estimation in IoT-Enabled Autonomous Ship

Abstract

Sea State Estimation (SSE) is essential for Internet of Things (IoT)-enabled autonomous ships, which rely on favorable sea conditions for safe and efficient navigation. Traditional methods, such as wave buoys and radars, are costly, less accurate, and lack real-time capability. Model-driven methods, based on physical models of ship dynamics, are impractical due to wave randomness. Data-driven methods are limited by the data imbalance problem, as some sea states are more frequent and observable than others. To overcome these challenges, we propose a novel data-driven approach for SSE based on ship motion data. Our approach consists of three main components: a data preprocessing module, a parallel convolution feature extractor, and a theoretical-ensured distance-based classifier. The data preprocessing module aims to enhance the data quality and reduce sensor noise. The parallel convolution feature extractor uses a kernel-varying convolutional structure to capture distinctive features. The distance-based classifier learns representative prototypes for each sea state and assigns a sample to the nearest prototype based on a distance metric. The efficiency of our model is validated through experiments on two SSE datasets and the UEA archive, encompassing thirty multivariate time series classification tasks. The results reveal the generalizability and robustness of our approach.