对比源反演算法对二维混合目标重建成像的应用

doi:10.3724/SP.J.1087.2012.01184

计算机应用 ›› 2012, Vol. 32 ›› Issue (04): 1184-1187.DOI: 10.3724/SP.J.1087.2012.01184

对比源反演算法对二维混合目标重建成像的应用

王学静¹,缪竟鸿¹,René Marklein²

1. 天津工业大学电子与信息工程学院,天津 300387
2. 卡塞尔大学电气工程与计算机系，德国卡塞尔 D-34121

收稿日期:2011-09-26 修回日期:2011-11-14 发布日期:2012-04-20 出版日期:2012-04-01
通讯作者: 王学静
作者简介:王学静（1978-）,女，天津人，讲师，硕士研究生，主要研究方向：线性和非线性算法、现代通信网；
缪竟鸿（1976-）,男，天津人，副教授，博士，主要研究方向：线性和非线性算法、电磁场理论、电磁逆散射；René Marklein（1964-），男，德国卡塞尔人，博士，IEEE高级会员，主要研究方向：声波、弹性波和电磁波的散射及逆散射。
基金资助:
国家自然科学基金项目;天津市自然科学基金项目

Application of contrast source inversion algorithm to image restruction of 2-D hybrid targets

WANG Xue-jing¹,MIAO Jing-hong¹,René Marklein²

1. School of Electronics and Information and Engineering, Tianjin Polytechnic University, Tianjin 300387, China
2. Department of Electrical Engineering and Computer Science, University of Kassel, Kassel D-34121, Germany

Received:2011-09-26 Revised:2011-11-14 Online:2012-04-20 Published:2012-04-01
Contact: WANG Xue-jing
Supported by:
the National Natural Science Foundation Project of China

摘要/Abstract

摘要： 针对线性成像精确性有限的缺点，提出了在非线性算法—对比源反演算法中采用正则化和并行频率方重建在暗室内的金属和电介质柱体构成的混合目标。二维实测微波散射数据采用了多频率、多收发设置的测量方式。通过目标的位置、形状及对比度值得展示，验证了扩展后的对比源反演算法对复杂的二维混合目标重建成像的精确性。

关键词: 对比源反演算法, 正则化, 并行频率, 多重信号分类算法, 时间反演成像

Abstract: In view of the limited accuracy of imaging algorithm，the nonlinear Contrast Source Inversion (CSI) algorithm combined with regularization and Concurrent Frequency (CF) was proposed for reconstructing a hybrid target in an anechoic chamber. The experimental data were obtained using multi-frequency multi-bistatic measurements. The reconstructed position, shape and contrast value of the target were presented, verifying the accuracy of the extended CSI algorithm for reconstructing the complicated 2-D hybrid targets.

Key words: Contrast Source Inversion (CSI) algorithm, regularization, Concurrent Frequency （CF）, Multiple Signal Classification (MUSIC) algorithm, Time Reversal (TR) imaging

王学静缪竟鸿 René Marklein. 对比源反演算法对二维混合目标重建成像的应用[J]. 计算机应用, 2012, 32(04): 1184-1187.

WANG Xue-jing MIAO Jing-hong René Marklein. Application of contrast source inversion algorithm to image restruction of 2-D hybrid targets[J]. Journal of Computer Applications, 2012, 32(04): 1184-1187.

参考文献

［1］ MARKLEIN R, MAYER K, HANNEMANN R, et al. Linear and nonlinear inversion algorithms applied in nondestructive evaluation ［J］. Inverse Problems, 2002,18(6): 1733-1759. ［2］ ABUBARKAR A, van den BERG P M. Iterative forward and inverse algorithms based on domain integral equations for three dimensional electric and magnetic objects ［J］. Journal of Computational Physics, 2004, 195(1):236-262. ［3］ YU C, SONG L P, LIU Q H. Inversion of multi-frequency experimental data for imaging complex objects by a DTA-CSI method ［J］. Inverse Problems, 2005, 21(6): S165-S178. ［4］ GEFFRIN J M, SABOUROUX P, EYRAUD C. Free space experimental scattering database continuation: Experimental set-up and measurement precision ［J］. Inverse Problems, 2005, 21(6):S117-S130. ［5］ MIAO J. Linear and nonlinear inverse scattering algorithms applied in 2-D electromagnetics and elastodynamics ［M］. Kassel: Kassel University Press, 2008:1-175. ［6］ CHENEY M. The linear sampling method and the MUSIC algorithm［J］. Inverse Problems, 2001, 17(4): 591-595. ［7］ PARK W, LESSELIER D. MUSIC-type imaging of a thin penetrable inclusion from its multi-response matrix ［J］. Inverse Problems, 2009, 25(7): 1-34. ［8］ MARENGO E A, GRUBER F K, SIMONETTI F. Time-reversal MUSIC imaging of extended targets ［J］. IEEE Transactions on Image Processing, 2007, 16(8):1967-1984. ［9］ KLEINMAN R E, van den BERG P M. A contrast source inversion method ［J］. Inverse Problems, 1997, 13(6):1607-1620. ［10］ EGGER H, LEITO A. Nonlinear regularization methods for ill-posed problems with piecewise constant or strongly varying solutions ［J］, Inverse Problems, 2009, 25(11): 1-19. ［11］ BACHMAYR M, BURGER M. Iterative total variation schemes for nonlinear inverse problems ［J］. Inverse Problems, 2009, 25(10):1-26. ［12］ ABUBARKAR A, HU W, van den BERG P M, et al. A finite-difference contrast source inversion method ［J］. Inverse Problems, 2008, 24(6): 1-17. ［13］ MIAO J, MARKLEIN R, LI J. Application of the linear and nonlinear inversion algorithms on two-dimentional experimental electromagnetic data ［C］// the International Conference on Microwave Technology and Computational Electromagnetics. Beijing:[s.n.], 2009:296-299.

[1]	林筠超, 万源. 基于图结构优化的自适应多度量非监督特征选择方法[J]. 计算机应用, 2021, 41(5): 1282-1289.
[2]	贺杰安, 吴晓红, 何小海, 胡建蓉, 卿粼波. 结合图像增强和卷积神经网络的小麦不完善粒识别[J]. 计算机应用, 2021, 41(3): 911-916.
[3]	汪志远, 降爱莲, 奥斯曼·穆罕默德. 基于正则互表示的无监督特征选择方法[J]. 计算机应用, 2020, 40(7): 1896-1900.
[4]	谢琪, 徐旭, 程耕国, 陈和平. 基于新的森林优化算法的特征选择算法[J]. 计算机应用, 2020, 40(5): 1266-1271.
[5]	耿源谦, 吴传生, 刘文. 混合非凸非光滑正则化约束的模糊图像盲复原[J]. 计算机应用, 2020, 40(4): 1171-1176.
[6]	曾梦, 宁彬, 蔡之华, 谷琼. 使用深度对抗子空间聚类实现高光谱波段选择[J]. 计算机应用, 2020, 40(2): 381-385.
[7]	张斌, 罗晓清, 张战成. 基于TV-L¹结构纹理分解的图像融合质量评价算法[J]. 计算机应用, 2019, 39(9): 2701-2706.
[8]	任珍文, 吴明娜. 基于熵自加权联合正则化最近点的图像集分类算法[J]. 计算机应用, 2019, 39(9): 2547-2551.
[9]	程俊华, 曾国辉, 鲁敦科, 黄勃. 基于Dropout的改进卷积神经网络模型平均方法[J]. 计算机应用, 2019, 39(6): 1601-1606.
[10]	李登刚, 陈香香, 李华丽, 王忠美. 基于超像素的流形正则化稀疏约束NMF混合像元分解算法[J]. 计算机应用, 2019, 39(10): 3100-3106.
[11]	冯明皓, 张天伦, 王林辉, 陈荣, 连少静. 改进的弹性网模型在深度神经网络中的应用[J]. 计算机应用, 2019, 39(10): 2809-2814.
[12]	文凯, 朱传亮. 融合社交网络和兴趣的正则化矩阵分解推荐模型[J]. 计算机应用, 2018, 38(9): 2523-2528.
[13]	代刚, 张鸿. 基于语义相关性与拓扑关系的跨媒体检索算法[J]. 计算机应用, 2018, 38(9): 2529-2534.
[14]	向文, 张灵, 陈云华, 姬秋敏. 结合结构自相似性和卷积网络的单幅图像超分辨率[J]. 计算机应用, 2018, 38(3): 854-858.
[15]	孙圣姿, 万源, 曾成. 自适应嵌入的半监督多视角特征降维方法[J]. 计算机应用, 2018, 38(12): 3391-3398.

对比源反演算法对二维混合目标重建成像的应用

Application of contrast source inversion algorithm to image restruction of 2-D hybrid targets

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics