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基于微多相粒子群算法的介质粗糙面反演研究 被引量：2

Investigation on the inversion of dielectric rough surface based on micro multi-phase particle swarm optimization
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摘要 为快速有效地求解介质粗糙面的反演问题,提出了一种能反演地表面参数的新方法——微多相粒子群算法。利用矩量法结合前后向迭代法快速求解双站散射系数。采用单纯形法实现有导向的约束初始种群的生成,以双站散射系数的测量值和理论计算值的偏差为目标函数,通过多相粒子群算法和单纯形相结合的算法对优化变量进行优化,使目标函数达到最小值实现地表参数的反演。给出了应用该方法的具体步骤,分析了测量结构,通过仿真实验验证了算法的有效性。 A novel approach known as micro multi-phase particle swarm optimization（μMPPSO） is applied to the inverse problem associated with dielectric rough surface.A scattering model based on the method of moment（MOM） and forward and back method（FBM） is used to solve the scattering problem.The generation of initial swarm is incorporated with the information obtained from simplex method.The error between the measured back scattering coefficient and the computed back scattering coefficient are considered as the object functions.The inverse scattering problem is transferred into an optimization problem by minimizing the object function with optimization parameters,which is solved by μMPPSO.The operation procedure is shown clearly;the set of measurement is analyzed,and the efficiency of new algorithm is validated by simulation.
作者 黄国荣 张传国 刘华伟
机构地区 空军工程大学工程学院 中国人民解放军
出处 《系统工程与电子技术》 EI CSCD 北大核心 2011年第2期249-252,338,共5页 Systems Engineering and Electronics
基金 国家自然科学基金(60304004) 空军工程大学教研新星培养计划基金资助课题
关键词 微多相粒子群算法 反演 前后向迭代法 单纯形法 micro multi-phase particle swarm optimization（μMPPSO） inversion forward and backward method（FBM） simplex method
分类号 TN011 [电子电信—物理电子学]
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参考文献7

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同被引文献17

  • 1金荣洪,袁智皓,耿军平,范瑜,李佳靖.基于改进粒子群算法的天线方向图综合技术[J].电波科学学报,2006,21(6):873-878. 被引量：32
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  • 3李正强.粒子群神经网络的并行实现及在微带天线设计中的应用[D].镇江:江苏科技大学,2009.
  • 4Ling S H, Chan K Y. Hybird Particle Swarm Optimization with Wavelet Mutation and Its Industrial Applications[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2008, 38(3): 743- 763.
  • 5Cui T J, Chew W C, Yin X X, et al. Study of resolution and super resolution in electromagnetic imaging for half-space problems[J]. IEEE Trans. on Antennas and Propagation, 2004, 52 (6) : 1398 - 1410.
  • 6Li F H, Liu Q H, Song L P. Three-dimensional reconstruction of objects buried in layered media using born and distorted born iterative methods[J]. IEEE Trans. on Geoscience and Remote Sensing Letters, 2004,1 (2) : 107 - 111.
  • 7Sahin A, Miller E L. Object detection using high resolution near-field array processing[J]. IEEE Trans. on Geoscience and Remote Sensing, 2001, 39(1) : 136 - 141.
  • 8Firoozabadi R, Miller E L, Rappaport C M. New inverse meth od for simultaneous reconstruction of object buried beneath rough ground and the ground surface structure using SAMM for- ward model[C]//Proc, of the SPIE-IS&T Electronic Ima ging, 2005, 5674:382-393.
  • 9Cmielewski O, Tortel H, Amelie L. A two-step procedure for characterizing obstacles under a rough surface from bistatic measurements[J]. IEEE Trans. on Geoscience and Remote Sensing, 2007,45(9):2850-2858.
  • 10Altuncu Y, Ozdemir O, Akduman I. Imaging of dielectric ob- jects buried under an arbitrary rough surface[C]//Proc, of the IEEE International Geoscience and Remote Sensing Syrnposi urn, 2006 :2954 - 2957.

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系统工程与电子技术
2011年 第2期

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