Wave Parameter Inversion Based on Sea Clutter Characteristics

Xiao Li

doi:10.3390/rs18162799

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Wave Parameter Inversion Based on Sea Clutter Characteristics

2026-08-28

DOI：10.3390/rs18162799

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Abstract

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Accurate acquisition of wave parameters is important for marine monitoring and target identification. Traditional methods are limited in sea state adaptability, real-time observation, and inversion accuracy. Tower-based non-scanning pulsed radar, as a low-cost near-shore method, provides all-weather, high temporal resolution observations without beam scanning. However, it yields two-dimensional range-pulse echo data blocks without azimuth information, making traditional wave spectrum inversion difficult to apply directly. To address this, we propose a wave parameter inversion method based on a ResNet–Transformer hybrid network. It takes sea clutter characteristics, radar parameters, and wind parameters as inputs, and uses 1D ResNet for local feature extraction and a Transformer encoder for cross-feature global interaction modeling, achieving simultaneous inversion of relative wave direction, mean wave period, and significant wave height. Experimental results on measured sea clutter samples demonstrate that the proposed method achieves high accuracy across all tasks. The RMSEs are 24.4 degrees, 0.5s, and 0.3 m for relative wave direction, mean wave period, and significant wave height. Experimental results on the measured dataset demonstrate that the model effectively captures the nonlinear mappings. These results support the feasibility of wave parameter estimation from tower-based non-scanning radar measurements under the evaluated conditions.

Keywords:

sea clutter

deep learning

wave parameter inversion

significant wave height