高炉浓尘环境料面散射特性建模与成像

doi:10.12305/j.issn.1001-506X.2023.05.03

摘要/Abstract

摘要：

工业恶劣成像环境下的雷达图像常存在对比度低和清晰度差等问题, 质量退化的图像直接影响计算机视觉任务的处理效果以及目标参数反演结果。因此, 对高炉料面后向散射进行建模, 并针对弥散浓尘环境下的随机粗糙料面成像, 提出一种基于衰减补偿和散射修正的成像方法。考虑微波沿空间路径的传播损耗, 在回波模型中加入环境因子, 采用瑞利散射和积分方程法分别求解衰减系数和粗糙面的后向散射系数, 从传输损耗和散射机理出发, 对图像进行损耗补偿以及目标像素亮度的提升, 减小图像因成像环境、目标距离以及表面起伏形状不同造成的信号强度差异。通过南钢3号高炉雷达数据验证算法, 目标像素亮度差异平均减小4.33 dB, 有效改善雷达成像中高炉径向距离起伏料面的成像性能。

关键词: 散射特性, 随机粗糙面, 损耗补偿, 高炉料面成像

Abstract:

In harsh industrial environments, radar imaging is subject to low contrast and blurred target outlines. The affected quality of the radar imaging can interfere the results of computer vision tasks and cause errors in the results of parameter inversion. In this paper, the back-scattering characteristics of blast furnace burden surface are modeled, and an imaging algorithm based on attenuation compensation and scatter correction is proposed to improve the image quality of random rough surface in a dense dust environment. Considering the propagation loss of microwave along the path, environmental factors are added to the echo model. The attenuation coefficient and the back-scattering coefficient are obtained by Rayleigh scattering and integral equation methods. Based on the transmission loss and scattering mechanism, the target pixel values are compensated, so that the contrast between the target and the background is improved. Differences in pixel values due to the imaging environment, objects at different distances, and the degree of surface inclination are reduced by the prposed algorithm, which is verified by the No. 3 blast furnace radar data of Nanjing Iron and Steel. The target pixel brightness difference is reduced by 4.33 dB on average, and the object imaging is more uniform and clearer.

Key words: scattering characteristics, random rough surface, loss compensation, blast furnace burden surface imaging

中图分类号:

TN959.5

于晴, 侯庆文, 陈先中. 高炉浓尘环境料面散射特性建模与成像[J]. 系统工程与电子技术, 2023, 45(5): 1277-1285.

Qing YU, Qingwen HOU, Xianzhong CHEN. Scattering characteristics modeling and imaging of blast furnace burden surface in dusty environment[J]. Systems Engineering and Electronics, 2023, 45(5): 1277-1285.

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样本类型	δ	l
大颗粒铁矿石粗糙面	0.010 1	0.038
小颗粒铁矿石粗糙面	0.005 5	0.029
大颗粒焦炭粗糙面	0.027 9	0.064
小颗粒焦炭粗糙面	0.015 7	0.045

对象	参数	取值
雷达系统	雷达位置/m	x_radar=3.9, y_radar=5
	中心频率/GHz	23.86
	带宽/GHz	1.51
	扫描角度/(°)	61
	扫频数/次	77
	距离门宽度/m	0.995
	介电常数	1.5+5i
炉内粉尘参数	单位体积颗粒数/个	20 000
炉内粉尘参数	颗粒粒径/mm	0.1

算例	料批	粗糙度参数/m	几何模型系数(炉径=4.6 m)
料面1	小颗粒矿石	δ=0.005 5, l=0.029	a₁=-0.47, b₁=-5.12 a₂=0.16, b₂=-0.03, c₂=-5.69 a₃=0.12, b₃=-5.24
料面2	大颗粒矿石	δ=0.010 1, l=0.038	a₁=-0.68, b₁=-5.05 a₂=0.56, b₂=-1.55, c₂=-4.71 a₃=0.25, b₃=-6.01
料面3	小颗粒焦炭	δ=0.015 7, l=0.045	a₁=-0.54, b₁=-4.85 a₂=0.59, b₂=-1.81, c₂=-4.15 a₃=0.16, b₃=-5.48

像素亮度差	料面1	料面2	料面3
传统成像	14.80	19.37	15.26
损耗补偿	12.76	17.32	13.17
本文成像	9.74	14.45	12.26

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