Wavelet-Discrete Cosine Transform Synergy for Ship Motion-Based Sea State Estimation in Autonomous Ships

Developing a robust autonomous sea state estimation (SSE) model stands as a pivotal challenge in advancing autonomous ships. Presently, deep learning (DL) methodologies have showcased remarkable efficacy in SSE tasks. Nonetheless, the dynamic nature of ship motion introduces temporal variations alongside frequency domain characteristics like periodic swinging, posing challenges for existing DL approaches. Most prevailing DL techniques, predominantly leveraging Convolutional Neural Networks or Long Short-Term Memory Networks, often fail to effectively harness frequency domain information post feature extraction. To tackle these limitations head-on, this paper introduces a pioneering SSE model. Specifically, in order to solve the frequency-domain feature extraction problem, we design a wavelet transform-based frequency domain encoder to extract relevant frequency-domain features from ship motion data by discriminating the contribution of different frequencies in the signal. Subsequently, in order to better integrate the extracted ship motion features, we designed a Feature Perception module based on discrete cosine transform. This module adeptly merges the extracted feature insights while prioritizing crucial frequency domain features. Following rigorous experimentation, our methodology exhibits superior performance compared to existing baseline techniques in SSE, a capability of profound significance for autonomous ships. Moreover, across diverse public multivariate time series classification datasets, our model outperforms current state-of-the-art approaches, underscoring its scalability across distinct domains. Note to Practitioners—Note to Practitioners (NtP): In the development of autonomous ships, precise SSE is crucial for ensuring safety and efficiency. The inherent uncertainties of open sea environments amplify the complexity of autonomous vessel navigation, presenting significant challenges. This paper introduces a DL model that leverages wavelet transform and discrete cosine transform to enhance the feature extraction of ship motion data. Unlike traditional methods that rely on manual feature extraction, our model autonomously captures both time-domain and frequency-domain features, thereby addressing the complexities of ship motion. In the discussion section of this paper, we focus on the model’s performance in handling the prevalent issue of sea state imbalance and its real-time inference capabilities. The results validate the model’s applicability and reliability, suggesting that practitioners can deploy this model on ships to better support the advancement of autonomous ships.