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OTH雷达图像的粗糙度指标及用于射频干扰自适应抑制

罗忠涛 郭人铭 郭杰 何子述 卢琨

罗忠涛, 郭人铭, 郭杰, 何子述, 卢琨. OTH雷达图像的粗糙度指标及用于射频干扰自适应抑制. 自动化学报, 2020, 46(x): 1−9 doi: 10.16383/j.aas.c190286
引用本文: 罗忠涛, 郭人铭, 郭杰, 何子述, 卢琨. OTH雷达图像的粗糙度指标及用于射频干扰自适应抑制. 自动化学报, 2020, 46(x): 1−9 doi: 10.16383/j.aas.c190286
Luo Zhong-Tao, Guo Ren-Ming, Guo Jie, He Zi-Shu, Lu Kun. Tamura coarseness for OTH radar image evaluation and its application in adaptive optimization of interference suppression. Acta Automatica Sinica, 2020, 46(x): 1−9 doi: 10.16383/j.aas.c190286
Citation: Luo Zhong-Tao, Guo Ren-Ming, Guo Jie, He Zi-Shu, Lu Kun. Tamura coarseness for OTH radar image evaluation and its application in adaptive optimization of interference suppression. Acta Automatica Sinica, 2020, 46(x): 1−9 doi: 10.16383/j.aas.c190286

OTH雷达图像的粗糙度指标及用于射频干扰自适应抑制

doi: 10.16383/j.aas.c190286
基金项目: 国家自然科学基金 (61701067, 61702065), 重庆市教育委员会科研基金 (KJ1600427, KJ1600429)资助
详细信息
    作者简介:

    罗忠涛:重庆邮电大学通信与信息工程学院讲师, 主要研究方向为信号与信息处理, 统计信号处理, 数字图像处理.E-mail: luozt@cqupt.edu.cn

    郭人铭:重庆邮电大学通信与信息工程学院硕士研究生, 主要研究方向为信号与信息处理, 数字图像处理.E-mail: s180131036@stu.cqupt.edu.cn

    郭杰:重庆邮电大学通信与信息工程学院学士, 主要研究方向为数字图像处理.E-mail: guojie1072050774@163.com

    何子述:电子科技大学电子工程学院教授, 主要研究方向为阵列信号处理, 自适应信号处理, MIMO雷达与通信.E-mail: zshe@uestc.edu.cn

    卢琨:中国电子科技集团有限公司第十四研究所研究员级高级工程师, 主要研究方向为超视距雷达系统设计和信息处理.E-mail: mimimomoba@gmail.com

Tamura Coarseness for OTH Radar Image Evaluation and Its Application in Adaptive Optimization of Interference Suppression

Funds: Supported by National Natural Science Foundation of China (61701067, 61702065), Project supported by Scientific Research Foundation of the Chongqing Education Committee (KJ1600427, KJ1600429)
  • 摘要: 针对OTH雷达距离-多普勒(Range-Doppler, RD)图, 本文首次提出采用纹理粗糙度作为RD图质量的评价指标, 即计算RD图所转化灰度图的Tamura纹理粗糙度. 分析表明, 粗糙度指标能准确反映RD图受干扰情况, 对于不同灰度转换函数具有稳健性. 作为应用举例, 本文将图像粗糙度用于改进射频干扰抑制算法, 使干扰抑制达到自适应优化. 实验结果表明, Tamura粗糙度能够正确反映RD图干扰抑制情况, 优化粗糙度指标能够使干扰抑制自适应达到最优.
  • 图  1  OTH雷达RD图

    Fig.  1  RD maps of OTH radar

    图  2  不同干扰情形下的RD灰度图: (a)-(c)分别表示无动态范围转换的无干扰、有窄带射频干扰、有宽带射频干扰的RD灰度图; (d)-(f)分别表示对应的动态范围为80 dB转换的RD灰度图

    Fig.  2  RD gray-scale images: (a)-(c) are images without dynamic range conversion, for no RFI, narrowband RFI, and wideband RFI, respectively; (d)-(f) are corresponding images with dynamic range conversion for 80 dB

    图  3  不同干扰抑制结果的RD灰度图: (a)-(e)分别表示无动态范围转换的无干扰、有宽带干扰、干扰未完全抑制、干扰完全抑制、干扰过度抑制的RD灰度图; (f)-(j)分别表示对应的动态范围为80 dB转换的RD灰度图

    Fig.  3  RD gray-scale image for various interference suppression results: (a)-(e) are images without interference, broadband RFI, incomplete suppression, complete suppression, and excessive suppression, without dynamic range conversion; (f)-(j) are the corresponding images with dynamic range conversion for 80 dB

    图  4  在无干扰的海杂波数据中加入目标回波后, RD图中20个目标的距离-多普勒单元位置

    Fig.  4  The position of 20 targets in the RD map without any RFI

    图  5  干扰抑制自适应优化流程框图

    Fig.  5  Flow chart of adaptive RFI suppression

    图  6  基于不同$ \varepsilon$参数下的RD灰度图与粗糙度

    Fig.  6  RD gray-scale image and coarseness for varying $ \varepsilon$

    图  7  粗糙度$ F$$ \varepsilon$的变化趋势

    Fig.  7  The coarseness $ F$ versus $ \varepsilon$

    图  8  不同$ K$值下$ \varepsilon$变化时的$ k_{best}$比例

    Fig.  8  The ratio of $ k_{best}$ versus $ \varepsilon$ for varying $ K$

    表  1  不同情况下RD图的粗糙度

    Table  1  Coarseness of RD images in various cases

    无动态转换时灰度级有动态转换时灰度级
    25664322566432
    无干扰6.926.926.916.846.846.83
    窄带RFI7.377.377.367.317.317.30
    宽带RFI8.588.588.558.588.588.56
    下载: 导出CSV

    表  2  不同情况下RD图的粗糙度

    Table  2  Coarseness of RD images in various cases

    无动态转换时灰度级有动态转换时灰度级
    25664322566432
    无干扰6.926.926.916.846.846.83
    有干扰8.588.588.558.588.588.56
    未完全抑制7.687.687.667.687.687.67
    完全抑制7.127.127.106.896.896.89
    过度抑制7.687.687.677.677.677.66
    下载: 导出CSV

    表  3  不同目标情况下RD图的粗糙度

    Table  3  Coarseness of RD images of various targets

    目标个数015101520
    无干扰6.846.846.856.866.876.89
    有干扰8.588.588.588.588.588.58
    未完全抑制7.687.687.697.697.697.70
    完全抑制6.896.896.906.916.926.94
    过度抑制7.677.677.697.697.707.71
    下载: 导出CSV

    表  4  滤波器自适应优化算法

    Table  4  Adaptive algorithm of filter optimization

    步骤操作流程
    步1设置滤波器参数$\varepsilon$的优化步长为$\triangle\varepsilon,$阈值比率$\eta\!=\!0.01.$初始化迭代次数$j\!=\!0,$对应滤波器参数为$\varepsilon_0\!=\!1,$计算干扰噪声区域的粗糙度值$F_0.$
    步2$j=j+1,\varepsilon_j=\varepsilon_{j-1}-\triangle\varepsilon,$根据$\varepsilon_j$设计滤波器并抑制干扰, 计算其RD灰度图的粗糙度$F_j.$
    步3计算粗糙度差值$\triangle F=F_{j-1}-F_j.$判断: 若$\triangle F/{F_{j-1}}<\eta,$取得最优抑制, 转至步4; 否则, 转至步2.
    步4$\triangle F<0,\varepsilon_{opt}=\varepsilon_{j-1};$否则, $\varepsilon_{opt}=\varepsilon_{j}.$输出$\varepsilon_{opt}$对应最优滤波器和干扰抑制结果.
    下载: 导出CSV
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