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基于支持向量回归和分位数的雷达K分布海杂波形状参数估计方法

薛健 孙孟玲 潘美艳

薛健, 孙孟玲, 潘美艳. 基于支持向量回归和分位数的雷达K分布海杂波形状参数估计方法[J]. 电子与信息学报, 2024, 46(4): 1399-1407. doi: 10.11999/JEIT230650
引用本文: 薛健, 孙孟玲, 潘美艳. 基于支持向量回归和分位数的雷达K分布海杂波形状参数估计方法[J]. 电子与信息学报, 2024, 46(4): 1399-1407. doi: 10.11999/JEIT230650
XUE Jian, SUN Mengling, PAN Meiyan. Shape Parameter Estimation of Radar K-distributed Sea Clutter Based on Support Vector Regression and Percentiles[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1399-1407. doi: 10.11999/JEIT230650
Citation: XUE Jian, SUN Mengling, PAN Meiyan. Shape Parameter Estimation of Radar K-distributed Sea Clutter Based on Support Vector Regression and Percentiles[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1399-1407. doi: 10.11999/JEIT230650

基于支持向量回归和分位数的雷达K分布海杂波形状参数估计方法

doi: 10.11999/JEIT230650
基金项目: 国家自然科学基金(62201455),陕西省科学技术协会青年人才托举计划(20230112),陕西省教育厅科研计划(22JK0566)
详细信息
    作者简介:

    薛健:男,副教授,研究方向为雷达杂波抑制、雷达目标检测分类识别等

    孙孟玲:女,硕士生,研究方向为雷达海杂波模型参数估计

    潘美艳:女,工程师,研究方向为杂波智能抑制、雷达目标识别等

    通讯作者:

    薛健 jxue@xupt.edu.cn

  • 中图分类号: TN959.72

Shape Parameter Estimation of Radar K-distributed Sea Clutter Based on Support Vector Regression and Percentiles

Funds: The National Natural Science Foundation of China (62201455), The Young Talent Fund of Association for Science and Technology in Shaanxi, China (20230112), The Scientific Research Program Funded by Shaanxi Provincial Education Department (22JK0566)
  • 摘要: 针对传统的雷达K分布海杂波形状参数估计方法在异常样本存在情况下估计精度严重下降的问题,该文提出一种基于支持向量回归(SVR)和样本分位数比值的K分布海杂波形状参数估计方法。首先给定K分布杂波参数和分位数位置的值,根据K分布的累积分布函数计算样本分位数比值及其对数,然后建立以样本分位数比值对数为输入、待估计形状参数为输出的SVR模型,通过交叉验证确定SVR模型的超参数,最后训练SVR模型实现对K分布海杂波形状参数的稳健精确估计。仿真和实测雷达数据表明,所提方法的估计误差低于基于矩的估计方法的估计误差,并且与基于分位数的估计方法具有相近估计性能。此外,相比已有基于分位数的方法,所提方法的超参数容易确定,并且不依赖于查表。
  • 图  1  SVR模型示意图

    图  2  所提估计方法的流程图

    图  3  使用不同核函数的拟合结果

    图  4  在不同形状参数$\eta $下所有方法估计结果的RRMSE

    图  5  当异常值比率为6%时,具有不同分位数所提出的SVR-LSPR的RRMSE

    图  6  实测海杂波数据分析(20210106155330_01_staring)

    图  7  实测海杂波数据分析(20210106155330_01_staring加入2%异常样本)

    图  8  实测海杂波数据分析(TFC17_002_08)

    图  9  实测海杂波数据分析(TFC17_002_08加入2 %异常样本)

    图  10  实测海杂波数据分析(19980217_224440_ANTSTEP)

    图  11  实测海杂波数据分析(19980217_224440_ANTSTEP加入2%异常样本)

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出版历程
  • 收稿日期:  2023-06-30
  • 修回日期:  2024-03-05
  • 网络出版日期:  2024-03-06
  • 刊出日期:  2024-04-24

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