Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Imagesстатья
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Дата последнего поиска статьи во внешних источниках: 30 января 2024 г.
Аннотация:Marine X-band radar is an important navigational tool that records signals reflected from thesea surface. Theoretical studies show that the initial unfiltered signal contains information about thesea surface state, including wind wave parameters. Physical laws describing the intensity of the signalreflected from the rough surface are the basis of the classical approaches for significant wave height (SWH)estimation. Nevertheless, the latest research claims the possibility of SWH approximation using machinelearning models. Both classical and AI-based approaches require in situ data collected during expensivesea expeditions or with wave monitoring systems. An alternative to real data is generation of synthetic radarimages with certain wind wave parameters. This Fourier-based approach is capable of modelling the seaclutter images for wind waves of any given height. Assuming a fully-developed sea, we generate syntheticimages from the Pierson–Moskowitz wave spectrum. After that, we apply an unsupervised learning usingsynthetic radar images to train the convolutional part of the neural network as the encoding part of theautoencoder. In this study, we demonstrate how the accuracy of SWH estimation based on radar imageschanges when the neural network is pretrained on synthetic data.