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基于CR下界无偏能量估计的高炉料面点云锐化成像

王倩 吴江雪 侯庆文 陈先中

王倩, 吴江雪, 侯庆文, 陈先中. 基于CR下界无偏能量估计的高炉料面点云锐化成像.自动化学报, 2021, 47(4): 839-848 doi: 10.16383/j.aas.c180683
引用本文: 王倩, 吴江雪, 侯庆文, 陈先中. 基于CR下界无偏能量估计的高炉料面点云锐化成像.自动化学报, 2021, 47(4): 839-848 doi: 10.16383/j.aas.c180683
Wang Qian, Wu Jiang-Xue, Hou Qing-Wen, Chen Xian-Zhong. Sharpness image of burden point cloud based on CR lower bound unbiased energy estimation. Acta Automatica Sinica, 2021, 47(4): 839-848 doi: 10.16383/j.aas.c180683
Citation: Wang Qian, Wu Jiang-Xue, Hou Qing-Wen, Chen Xian-Zhong. Sharpness image of burden point cloud based on CR lower bound unbiased energy estimation. Acta Automatica Sinica, 2021, 47(4): 839-848 doi: 10.16383/j.aas.c180683

基于CR下界无偏能量估计的高炉料面点云锐化成像

doi: 10.16383/j.aas.c180683
基金项目: 

国家自然科学基金 61671054

北京市自然科学基金 4182038

详细信息
    作者简介:

    王倩  北京科技大学自动化学院硕士研究生. 主要研究方向为移动边缘计算, 计算卸载. E-mail: wq_545496@126.com

    吴江雪  北京科技大学自动化学院硕士研究生. 主要研究方向为深度学习, 图像处理.E-mail: s20170612@xs.ustb.edu.cn

    侯庆文  北京科技大学自动化学院副教授, 主要研究方向为传感器技术, 信号处理, 嵌入式系统应用.E-mail: houqw@ustb.edu.cn

    通讯作者:

    陈先中  北京科技大学自动化学院教授.主要研究方向为电磁场与微波技术, 工业雷达探测与成像, 工业物联网与软件开发.本文通信作者.E-mail: cxz@ustb.edu.cn

Sharpness Image of Burden Point Cloud Based on CR Lower Bound Unbiased Energy Estimation

Funds: 

National Natural Science Foundation of China 61671054

Beijing Natural Science Foundation 4182038

More Information
    Author Bio:

    WANG Qian  Master student at the School of Automation, Beijing University of Science and Technology. Her research interest covers mobile edge computing and computation offloading

    WU Jiang-Xue  Master student at the School of Automation, Beijing University of Science and Technology. Her research interest covers deep learning and image processing

    HOU Qing-Wen  Associate professor at the School of Automation, Beijing University of Science and Technology. Her research interest covers sensor technology, signal processing, and embedded system application

    Corresponding author: CHEN Xian-Zhong  Professor at the School of Automation, Beijing University of Science and Technology. His research interest covers electromagnetic field and microwave technology, industrial radar detection and imaging, industrial Internet of things, and software development. Corresponding author of this paper
  • 摘要: 高炉雷达获取的料面信息是钢铁冶炼中布料控制的重要参数.但高炉内部环境复杂, 料面具有非均匀流态化特性, 传统信号处理方法难以准确稳定提取料面有效信息, 会导致高炉布料误操作.本文借鉴遥感SAR雷达成像原理, 设计了工业SAR扫描式雷达, 多倍增加料面采样点密度, 提出一种新的料面点云锐化成像算法. 分析了高炉雷达料面回波信号干扰信号特征, 从图像处理角度, 设计多级滤波器对2D频谱图进行去噪处理分离出一条带状的料面回波信号区域. 对料面距离估计问题, 基于克拉美罗下界(Cramer-Rao lower bound, CRLB)提出一种先加权采样锐化料带峰脊再利用能量重心法估测料面距离频率的方法, 生成3D料面点云模型, 并利用CRLB评价本文算法性能.在恶劣条件下, 实测高炉雷达料面回波信号的点云成像验证显示, 本文方法优于传统寻峰法, 能有效处理低信噪比信号, 准确提取料面有效信息.同时料面距离频率估计精度更高, 且相较于其他方法频率估计误差更接近CRLB下界.
    Recommended by Associate Editor XU De
    1)  本文责任编委 徐德
  • 图  1  雷达安装及扫射范围示意图

    Fig.  1  Radar installation and distance diagram

    图  2  原始雷达信号频谱对比

    Fig.  2  Comparison of original radar signal spectrum

    图  3  料面径向回波信号2D频谱图对比

    Fig.  3  Comparison of 2D spectra of radial echo signal of blast surface

    图  4  扇形空间下料面回波信号2D原始频谱图对比

    Fig.  4  Comparison of 2D original spectra of fan-shaped space blanking surface echo signal

    图  5  三维空间料面回波信号强度峰脊分布

    Fig.  5  Peak ridge distribution of echo signal strength in three dimensional space

    图  6  高炉料面点云锐化成像算法流程图

    Fig.  6  Algorithm flow chart of burden surface point cloud sharpen image processing

    图  7  三种滤波模型效果

    Fig.  7  Three filtering model effects

    图  8  高炉雷达的现场安装图

    Fig.  8  Field installation of the blast furnace radar

    图  9  高炉料面电磁散射图

    Fig.  9  Electromagnetic scattering image of burden surface

    图  10  能量点提取与料线拟合(Data1为峰脊锐化后的标志点, Data2为提取的能量点)

    Fig.  10  Energy point extraction and material line fitting (Data1 is the mark point after peak ridge sharpening, and Data2 is the energy point extracted)

    图  11  不同信噪比下各算法频率估计性能

    Fig.  11  The performance of each algorithm under different SNR is estimated

    图  12  3D料面点云成像效果

    Fig.  12  Imaging effect of 3D surface point cloud

    表  1  三种滤波模型

    Table  1  Three filtering models

    MWATF VITF MWITF
    中值法 方差法 中值法
    窗函数法 窗函数法
    人工阈值法 迭代阈值滤波法 人工阈值法
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-10-22
  • 录用日期:  2019-01-30
  • 刊出日期:  2021-04-23

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