华志恒,张金鹏,殷波,等. 复杂时空场景下的海杂波幅度分布一体化预测方法[J]. 电波科学学报,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2023304
      引用本文: 华志恒,张金鹏,殷波,等. 复杂时空场景下的海杂波幅度分布一体化预测方法[J]. 电波科学学报,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2023304
      HUA Z H, ZHANG J P, YIN B, et al. An integrated prediction method for sea clutter amplitude distribution in complex spatio-temporal scenarios[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2023304
      Citation: HUA Z H, ZHANG J P, YIN B, et al. An integrated prediction method for sea clutter amplitude distribution in complex spatio-temporal scenarios[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2023304

      复杂时空场景下的海杂波幅度分布一体化预测方法

      An integrated prediction method for sea clutter amplitude distribution in complex spatio-temporal scenarios

      • 摘要: 海杂波特性受不同时空下的海洋气象环境的影响而发生变化,体现出显著的时空非均匀性,严重影响雷达系统在不同海域的探测能力。本文针对海杂波幅度分布特性的描述须同时依赖分布类型和分布参数的特点,基于深度学习技术,提出了基于多任务并行学习的海杂波幅度分布一体化预测方法,引入抑制负值的损失函数改善分布参数预测的负值输出问题,实现海杂波幅度分布类型及分布参数并行化预测。南海实测S波段海杂波数据集和相同时空的海洋气象要素数据集的训练测试结果表明,本文方法可以基于时空场景变化的海洋气象环境参数,完成海杂波幅度分布类型及参数的并行预测,实现大尺度海域时空场景下的海杂波幅度分布特性预测。

         

        Abstract: The characteristics of sea clutter undergo changes influenced by the spatial-temporal variations in marine meteorological conditions, exhibiting significant spatial-temporal non-uniformity. This severely impacts the detection capability of radar systems in different maritime domains. In this study, considering the description of sea clutter amplitude distribution relies on both distribution types and parameters, this paper proposes an integrated prediction method for sea clutter amplitude distribution based on multi-task parallel learning using deep learning technology. To address the issue of negative output values in parameter prediction, a loss function that suppresses negative values is introduced. This enables parallel prediction of sea clutter amplitude distribution types and parameters. Through training and testing on a dataset from the South China Sea comprising S-band sea clutter data and spatio-temporally corresponding marine meteorological parameter data, the results indicate that this approach can effectively predict sea clutter amplitude distribution types and parameters in spatio-temporal scenarios influenced by changing marine meteorological conditions. This method facilitates the prediction of sea clutter amplitude distribution characteristics in large-scale maritime regions with varying spatio-temporal conditions.

         

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