海面角反射体电磁散射特性与雷达鉴别研究进展与展望

李郝亮 陈思伟

李郝亮, 陈思伟. 海面角反射体电磁散射特性与雷达鉴别研究进展与展望[J]. 雷达学报, 2023, 12(4): 738–761. doi: 10.12000/JR23100
引用本文: 李郝亮, 陈思伟. 海面角反射体电磁散射特性与雷达鉴别研究进展与展望[J]. 雷达学报, 2023, 12(4): 738–761. doi: 10.12000/JR23100
LI Haoliang and CHEN Siwei. Electromagnetic scattering characteristics and radar identification of sea corner reflectors: Advances and prospects[J]. Journal of Radars, 2023, 12(4): 738–761. doi: 10.12000/JR23100
Citation: LI Haoliang and CHEN Siwei. Electromagnetic scattering characteristics and radar identification of sea corner reflectors: Advances and prospects[J]. Journal of Radars, 2023, 12(4): 738–761. doi: 10.12000/JR23100

海面角反射体电磁散射特性与雷达鉴别研究进展与展望

doi: 10.12000/JR23100
基金项目: 国家自然科学基金(62122091, 61771480),国家部委基金
详细信息
    作者简介:

    李郝亮,博士生,主要研究方向为极化雷达成像与目标识别

    陈思伟,教授,博士生导师,主要研究方向为极化雷达成像与目标识别、机器学习、电子对抗等

    通讯作者:

    陈思伟 chenswnudt@163.com

  • 责任主编:杨健 Corresponding Editor: YANG Jian
  • 中图分类号: TN958

Electromagnetic Scattering Characteristics and Radar Identification of Sea Corner Reflectors: Advances and Prospects

Funds: The National Natural Science Foundation of China (62122091, 61771480), The National Ministries Foundation
More Information
  • 摘要: 雷达导引头是精确制导武器末制导的核心设备,具有作用距离远、不受天气影响等重要优点,在保证导弹打击精度方面发挥着重要作用。海面角反射体具有与舰船目标散射逼真度高、作战效费比高等优良特性,已成为雷达导引头的主要诱骗干扰手段之一,严重影响雷达目标探测性能。因此,如何准确高效地实现海面角反射体雷达鉴别是雷达导引头精确打击的难点和重点之一。角反射体电磁散射特性研究是提升角反射体雷达鉴别能力的基础。该文首先介绍了海面角反射体装备及战术运用;针对海面角反射体的电磁散射特性研究进展进行了总结;重点归纳梳理了海面角反射体雷达鉴别技术的两类主流方法,总结其特点及存在的问题;最后对海面角反射体雷达鉴别研究的未来发展趋势进行了展望。

     

  • 图  1  AN/SLQ-49充气式角反射体系统[6]

    Figure  1.  AN/SLQ-49 inflatable corner reflector system[6]

    图  2  海面充气式角反射体系统[12]

    Figure  2.  Sea inflatable corner reflector system[12]

    图  3  冲淡式干扰和质心式干扰示意图

    Figure  3.  Diagram of diluted jamming and centroid jamming

    图  4  海面角反射体多路径散射示意图

    Figure  4.  Schematic of multipath scattering from sea corner reflector

    图  5  TDSBR时域回波仿真结果[68]

    Figure  5.  Time domain echo simulation results by TDSBR[68]

    图  6  八面体角反射体RCS仿真结果

    Figure  6.  RCS simulation results from octahedral corner reflector

    图  7  二十面体角反射体RCS仿真结果

    Figure  7.  RCS simulation results from icosahedral corner reflector

    图  8  海面与目标的复合模型[82]

    Figure  8.  Composite models from sea surface and target[82]

    图  9  海面与八面体角反射体复合模型的极化散射分布[82]

    Figure  9.  Polarimetric scattering distribution of composite model from sea surface and octahedral corner reflector[82]

    图  10  海面与二十面体角反射体复合模型的极化散射分布[82]

    Figure  10.  Polarimetric scattering distribution of composite model from sea surface and icosahedral corner reflector[82]

    图  11  “海面-舰船-角反射体”复合模型的HRRP仿真结果

    Figure  11.  HRRP simulation results of the ‘sea surface-ship-corner reflector’ composite model

    图  12  “海面-舰船-角反射体”复合模型的二维成像仿真结果

    Figure  12.  2D imaging simulation results of the ‘sea surface-ship-corner reflector’ composite model

    图  13  极化雷达角反射体干扰仿真数据构建流程

    Figure  13.  Flowchart of polarimetric radar corner reflector jamming simulation data construction

    图  14  HRRP特征可分性

    Figure  14.  Separability of HRRP features

    图  15  基于多普勒特性鉴别角反射体干扰[84]

    Figure  15.  Corner reflector jamming identification based on Doppler characteristics[84]

    图  16  舰船和角反射体时频分布图[66]

    Figure  16.  Time-frequency distribution of ship and corner reflector[66]

    图  17  基于MF特征的鉴别结果[92]

    Figure  17.  Identification results with MF feature[92]

    图  18  不同特征集尺寸下的分类正确率[103]

    Figure  18.  Classification accuracy with different feature set sizes[103]

    图  19  角反射体与舰船的极化旋转域特征流形图

    Figure  19.  Characteristic manifold of corner reflector and ship in polarimetric rotation domain

    图  20  不同角反射体鉴别方法与PCP的对比结果[14]

    Figure  20.  Comparison results among different corner reflector identifier with PCP[14]

    图  21  舰船和角反射体的极化特征信干比

    Figure  21.  SJR values of the polarimetric features of ships and corner reflectors

    图  22  不同分类器与ELM的对比结果[128]

    Figure  22.  Comparison results between different classifiers and ELM[128]

    图  23  4种集成算法分类结果对比[10]

    Figure  23.  Comparison of classification results from four ensemble algorithms[10]

    表  1  用于角反射体雷达鉴别的HRRP特征归纳表

    Table  1.   Summary of HRRP features for corner reflector radar identification

    特征特征公式变量含义
    径向尺寸$ {\text{RL}} = \Delta d \cdot ({n_2} - {n_1}) $$ \Delta d $为距离分辨率,$ {n_1} $和$ {n_2} $分别为目标第1个和
    最后一个距离单元序号
    散射重心${\text{SM} } = \left(\left(\displaystyle\sum\limits_{n = {n_1} }^{ {n_2} } {n \cdot x(n)} \Bigr/ \displaystyle\sum\limits_{n = {n_1} }^{ {n_2} } {x(n)} \right) - {n_1}\right) \Bigr/ ({n_2} - {n_1})$$ x(n) $为目标距离单元幅值
    散射中心数目$ {\text{NP}} = \displaystyle\sum\limits_{{n_1}}^{{n_2}} {u(n)} $$ u(n) $为散射中心标记
    最强散射中心间距离$ {\text{DPK}} = \Delta d \cdot \left| {{m_1} - {m_2}} \right| $$ {m_1} $和$ {m_2} $为最强的两个散射中心对应的序号
    最强散射中心距目标
    最前端的距离
    $ {\text{DEP}} = \Delta d \cdot ({m_1} - {n_1}) $/
    散射中心幅值分布熵${\text{EA} } = - \displaystyle\sum\limits_{i = 1}^{{\rm{NP}}} { {p_{ {m_i} } } \cdot \ln {p_{ {m_i} } } }$$ {p_{{m_i}}} $为序号$ {m_i} $的强散射中心幅值
    散射中心位置分布熵${\text{EP} } = - \displaystyle\sum\limits_{i = 1}^{ {\rm{NP} } } { { m'_i} \cdot \ln { m'_i} }$$ {m'_i} = {{\left( {{m_i} - {n_1}} \right)} \mathord{\left/ {\vphantom {{\left( {{m_i} - {n_1}} \right)} {\left( {{n_2} - {n_1}} \right)}}} \right. } {\left( {{n_2} - {n_1}} \right)}} $
    散射对称性值$ {{{\text{SYM}} = \displaystyle\sum\limits_{n = {n_1}}^{{n_0}} {{{\left| {x(n)} \right|}^2}} } \mathord{\left/ {\vphantom {{{\text{SYM}} = \displaystyle\sum\limits_{n = {n_1}}^{{n_0}} {{{\left| {x(n)} \right|}^2}} } {\displaystyle\sum\limits_{n = {n_0}}^{{n_2}} {{{\left| {x(n)} \right|}^2}} }}} \right. } {\displaystyle\sum\limits_{n = {n_0}}^{{n_2}} {{{\left| {x(n)} \right|}^2}} }} $$ {n_0} = {{({n_1} + {n_2})} \mathord{\left/ {\vphantom {{({n_1} + {n_2})} 2}} \right. } 2} $
    下载: 导出CSV

    表  2  用于角反射体雷达鉴别的极化特征归纳表

    Table  2.   Summary of polarimetric features for corner reflector radar identification

    特征特征公式变量含义
    Krogager分解特征$k_{\text{S}}^{} = \left| {{S_{{\text{RL}}}}} \right|$, $ k_{\text{D}}^{} = \min \left( {\left| {{S_{{\text{LL}}}}} \right|,\left| {{S_{{\text{RR}}}}} \right|} \right) $, $ k_{\text{H}}^{} = \left| {\left| {{S_{{\text{RR}}}}} \right| - \left| {{S_{{\text{LL}}}}} \right|} \right| $$ {S_{{\text{LL}}}} $, $ {S_{{\text{RR}}}} $和${S_{{\text{RL}}}}$为圆极化复散射系数
    Pauli分解特征$ {p_{\rm{a}}} = \dfrac{{{S_{{\text{HH}}}} + {S_{{\text{VV}}}}}}{{\sqrt 2 }} $, $ {p_{\rm{b}}} = \dfrac{{{S_{{\text{HH}}}} - {S_{{\text{VV}}}}}}{{\sqrt 2 }} $, $ {p_{\rm{c}}} = \dfrac{{{S_{{\text{HV}}}} + {S_{{\text{VH}}}}}}{{\sqrt 2 }} $$ {S_{{\text{HH}}}} $, $ {S_{{\text{VV}}}} $, $ {S_{{\text{HV}}}} $和$ {S_{{\text{VH}}}} $为线极化复散射系数
    Cameron分解特征$d({z_1},{z_2}) = \arccos \left( {\dfrac{ {\max \left\{ {\left| {1 + {z_1}z_2^*} \right|,\left| { {z_1} + z_2^*} \right|} \right\} } }{ {\sqrt {1 + { {\left| { {z_1} } \right|}^2} } \cdot\sqrt {1 + { {\left| { {z_2} } \right|}^2} } } } } \right)$$ {z_1} $和$ {z_2} $分别为待分类散射体和典型散射体的
    散射类型参数,$* $表示共轭
    Cloude-Pottier分解
    特征
    $ H = - \displaystyle\sum\limits_{i = 1}^3 {{P_i}} {\log _3}{P_i} ,\; \bar \alpha = \displaystyle\sum\limits_{i = 1}^3 {{P_i}{\alpha _i}}, $ ${\rm{Ani} } = \dfrac { {\lambda _2} - {\lambda _3} } { { {\lambda _2} + {\lambda _3} } }$ $ {P_i} $为伪概率密度,$ {\alpha _i} $为特征矢量的系数因子,
    $ {\lambda _i} $为特征值
    行列式模$\left| \varDelta \right| = \left| { {S_{ {\text{HH} } } }{S_{ {\text{VV} } } } - {S_{ {\text{HV} } } }{S_{ {\text{VH} } } } } \right|$/
    极化总功率${\text{SPAN}} = {\left| {{S_{{\text{HH}}}}} \right|^2} + {\left| {{S_{{\text{HV}}}}} \right|^2} + {\left| {{S_{{\text{VH}}}}} \right|^2} + {\left| {{S_{{\text{VV}}}}} \right|^2}$/
    去极化因子$ D = 1 - \dfrac{{{{\left| {{S_{{\text{HH}}}} + {S_{{\text{VV}}}}} \right|}^2}}}{{2\left( {{{\left| {{S_{{\text{HH}}}}} \right|}^2} + {{\left| {{S_{{\text{HV}}}}} \right|}^2} + {{\left| {{S_{{\text{VH}}}}} \right|}^2} + {{\left| {{S_{{\text{VV}}}}} \right|}^2}} \right)}} $/
    本征极化方位角${\theta _{\rm{d}}} = \dfrac{1}{2}\arctan \dfrac{ {2{{\rm{Re}}} \left( {\tilde S_1^*{ {\tilde S}_{12} } } \right)} }{ { {{\rm{Re}}} \left( {\tilde S_1^*{ {\tilde S}_2} } \right)} }$$ {\tilde S_1} = {S_{{\text{HH}}}} + {S_{{\text{VV}}}} $, $ {\tilde S_2} = {S_{{\text{HH}}}} - {S_{{\text{VV}}}} $, $ {\tilde S_{12}} = {S_{{\text{HV}}}} $
    本征极化椭圆度${\alpha _{\rm{d}}} = \dfrac{1}{2}\arctan \dfrac{ {{\rm{j}}2\tilde S_{12}^\prime } }{ { {S_{ {\text{HH} } } } + {S_{ {\text{VV} } } } } }$$\tilde S_{12}^\prime = {\tilde S_{12} }\cos \left( {2{\theta _{\rm{d}}} } \right) - \dfrac{1}{2}{\tilde S_2}\sin \left( {2{\theta _{\rm{d}}} } \right)$
    目标纵横比$\eta = \dfrac{{{{\left| {{S_{{\text{HH}}}}} \right|}^2} + {{\left| {{S_{{\text{HV}}}}} \right|}^2} + {{\left| {{S_{{\text{VH}}}}} \right|}^2} + {{\left| {{S_{{\text{VV}}}}} \right|}^2}}}{{\left| {{S_{{\text{HH}}}}{S_{{\text{VV}}}} - {S_{{\text{HV}}}}{S_{{\text{VH}}}}} \right|}}$/
    目标极化形状因子$ \gamma = \dfrac{{\left| {{S_{{\text{VV}}}}S_{{\text{HH}}}^* - {S_{{\text{VH}}}}S_{{\text{HV}}}^*} \right|}}{{{{\left| {{S_{{\text{HH}}}}} \right|}^2} + {{\left| {{S_{{\text{HV}}}}} \right|}^2} + {{\left| {{S_{{\text{VH}}}}} \right|}^2} + {{\left| {{S_{{\text{VV}}}}} \right|}^2}}} $/
    极化相关度 ${\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{mean} } } } = {\text{mean} }\left\{ {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|} \right\}$ $\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|$为极化相关方向图,$ {\text{mean}}\left\{ \cdot \right\} $为求均值
    极化相关特征最小值${\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{min} } } } = {\text{min} }\left\{ {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|} \right\}$$ {\text{min}}\left\{ \cdot \right\} $为求最小值
    极化相关对比度 ${\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{contrast} } } } = {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{max} } } } - {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{min} } } }$ ${\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{max} } } }$为极化相关特征最大值
    极化相关特征反熵${\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{\text{A} } } = \dfrac{ { { {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|}_{ {\text{max} } } } - { {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|}_{ {\text{min} } } } } }{ { { {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|}_{ {\text{max} } } } + { {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|}_{ {\text{min} } } } } }$/
    极化相关起伏度${\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|_{ {\text{std} } } } = {\text{std} }\left\{ {\left| { { {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$} }{\gamma } }_{ {\text{HH} } {\text{-}} {\text{VV} } } }\left( \theta \right)} \right|} \right\}$$ {\text{std}}\left\{ \cdot \right\} $为求标准差
    下载: 导出CSV

    表  3  角反射体雷达鉴别方法适用场景和优缺点总结

    Table  3.   Applicable scenarios and pros & cons summary of corner reflector radar identification

    关键
    技术
    鉴别
    分类
    具体鉴别方法适用场景优点缺点
    基于特征提取的角反射体鉴别方法HRRP
    特征
    连续统计跟踪算法质心干扰[84]简单易实现,计算效率高,具有实时性对目标信息利用不充分
    基于平移不变特征的
    角反鉴别
    冲淡干扰[85]融合空间邻域信息,提高目标鉴别能力敏感于雷达观测角度且不适用于角反射体阵列
    基于稀疏表达的
    角反鉴别
    冲淡干扰[86]模型简单,受噪声影响小对目标姿态的适应性有待研究
    运动
    特征
    基于多普勒特征的
    角反鉴别
    质心干扰[84]/
    冲淡干扰[84,87]
    增强了对运动目标鉴别能力不适用于拖曳式角反射体
    基于微多普勒特征的
    角反鉴别
    冲淡干扰[37,91]海面目标和角反射体由于结构和尺寸不同,在微动特征上存在较大差异对海面强杂波与目标运动变化敏感,对目标微多普勒的观测本身需要的条件苛刻
    极化
    特征
    基于极化目标分解的
    角反鉴别
    冲淡干扰[99,100,132]具有较强的物理可解释性存在散射机理解译失真
    基于极化旋转不变量
    的角反鉴别
    冲淡干扰[105,106]可以避免雷达观测角度的依赖性部分极化不变量敏感于目标尺寸
    基于极化旋转域的
    角反鉴别
    冲淡干扰[14,82,119121]充分利用了雷达目标的散射多样性中蕴含的丰富的极化散射信息对海况状态的适应性需要提高
    基于极化域变焦的
    角反鉴别
    质心干扰[123]/
    冲淡干扰[122]
    有效提升雷达信息获取能力理论分析和方法实现复杂
    基于深度学习的角反射体鉴别方法//冲淡干扰[10,85,106,127132]能够自动提取结构化特征,鉴别率高需要大量标记样本且缺乏可解释性
    其他
    方法
    /基于波形选择的
    角反鉴别
    冲淡干扰[133]可以自适应优化不同场景下的鉴别性能复杂结构和成本限制工程应用
    基于复合制导的
    角反鉴别
    质心干扰[136]/冲淡干扰[136]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-31
  • 修回日期:  2023-07-05
  • 网络出版日期:  2023-07-27
  • 刊出日期:  2023-08-28

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