Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting, sea state prediction, and safe navigation. However, vessel motion and challenging environmental conditions often affect measurement precision. To address this issue, this study proposes an innovative framework for correcting and predicting shipborne wind speed. By integrating a main network with a momentum updating network, the proposed framework effectively extracts features from the time and frequency domains, thereby allowing for precise adjustments and predictions of shipborne wind speed data. Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single- and multi-step predictions compared to existing methods, achieving higher accuracy in wind speed forecasting. The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.