高频地波雷达对近岸区域进行探测时, 雷达电磁波往往容易受到海岸线以及岛屿等地形因素的干扰, 从而导致雷达探测数据合成的海流方向出现较大的误差, 而传统的高频地波雷达反演算法并未将物理因素的影响纳入其中。为了解决这个问题, 结合卷积神经网(convolutional neural networks, CNN)和长短期记忆(long short-term memory, LSTM)神经网络的特点, 本文提出了一种基于CNN-LSTM的组合模型, 将海面风、潮汐以及海拔数据输入到模型中, 对雷达探测数据合成的海流方向进行校正。实验结果表明, CNN-LSTM模型能够有效提高受地形影响区域的雷达数据质量, 使合成的海流方向准确性显著提升。

High-frequency surface wave radar often encounters interference from coastal topography and islands when detecting nearshore areas, leading to significant errors in the synthesized ocean current directions from radar detection data. Traditional high-frequency surface wave radar inversion algorithms do not account for the impact of these physical factors. To address this issue, leveraging the strengths of convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks, this paper introduces a hybrid model dubbed CNN-LSTM. This model takes in data on sea surface wind, tide, and elevation, allowing for the refinement of ocean current directions derived from radar detection data. Experimental results show that the CNN-LSTM model can effectively improve the quality of radar data in areas affected by topography, significantly enhancing the accuracy of the synthesized ocean current directions.