刘昆, 杨了, 夏晴, 唐涛. 基于神经网络的导体柱单站微波成像[J]. 电波科学学报, 2012, 27(5): 1056-1060.
      引用本文: 刘昆, 杨了, 夏晴, 唐涛. 基于神经网络的导体柱单站微波成像[J]. 电波科学学报, 2012, 27(5): 1056-1060.
      LIU Kun, YANG Liao, XIA Qing, TANG Tao. Monostatic microwave imaging method for conductor cylinder based on neural network[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2012, 27(5): 1056-1060.
      Citation: LIU Kun, YANG Liao, XIA Qing, TANG Tao. Monostatic microwave imaging method for conductor cylinder based on neural network[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2012, 27(5): 1056-1060.

      基于神经网络的导体柱单站微波成像

      Monostatic microwave imaging method for conductor cylinder based on neural network

      • 摘要: 提出基于神经网络对导体柱目标实现单站微波成像的方法。通过时域有限差分方法获得横磁波(TM)模式下一系列不同形状导体柱目标的散射场,组成神经网络训练样本。再通过组合运用整数微分进化策略和误差反向传播(BP)算法形成混合自学习策略,克服传统BP神经网络收敛速度慢等不足,提高系统自学习能力。最终基于所得神经网络获得二维导体柱微波成像结果。

         

        Abstract: A microwave imaging method for monostatic using neural network is proposed in this paper.Firstly,the TM mode scattering data of different shapes of conductor cylinder are obtained by finite-difference time-domain(FDTD)method.And all these data are taken as training samples for neural network.A hybrid self-learning strategy based on integral differential evolution strategy and BP algorithm is presented, which can improve self-learning capability of system and overcome the drawbacks of single BP neural network, such as slow astringency.Finally,the satisfactory results of two-dimensional microwave imaging are reached by using the optimized neural network.

         

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