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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, 2022, 48(3): 887−895 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, 2022, 48(3): 887−895 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, 2022, 48(3): 887−895 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, 2022, 48(3): 887−895 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:博士, 重庆邮电大学通信与信息工程学院副教授. 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. 本文通信作者.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:博士, 电子科技大学信息与通信工程学院教授. 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:博士, 南京电子技术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) and Scientific Research Foundation of the Chongqing Education Committee (KJ1600427, KJ1600429)
More Information
    Author Bio:

    LUO Zhong-Tao Ph.D., associate professor at the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications. His research interest covers signal and information processing, statistical signal processing, and digital image processing. Corresponding author of this paper

    GUO Ren-Ming Master student at the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications. His research interest covers signal and information processing, and digital image processing

    GUO Jie Bachelor at the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications. His research interest covers digital image processing

    HE Zi-Shu Ph.D., professor at the School of Information and Communication Engineering, University of Electronic Science and Technology of China. His research interest covers array signal processing, adaptive signal processing, MIMO radar, and MIMO communication

    LU Kun Ph.D., fellow senior engineer at Nanjing Research Institute of Electronics Technology. His research interest covers system design and signal processing for over-the-horizon radar

  • 摘要: 针对OTH (Over-the-horizon) 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_{{\rm{best}}}$比例

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

    表  1  不同干扰类型的RD图粗糙度

    Table  1  Coarseness of RD images for various kinds of interference

    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  不同干扰抑制程度的RD图粗糙度

    Table  2  Coarseness of RD images for various levels of RFI suppression

    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
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出版历程
  • 收稿日期:  2019-04-08
  • 网络出版日期:  2022-01-24
  • 刊出日期:  2022-03-25

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