Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space

doi:10.3390/s24072322

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/125884

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题名:	Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space
作者:	Chiu, Chien-Ching;Lee, Yang-Han;Chen, Po-Hsiang;Shih, Ying-Chen;Hao, Jiang
关键词:	inverse scattering problem;self-attention;generative adversarial network;real-time imaging;back-propagation scheme
日期:	2024-04-05
上传时间:	2024-08-07 12:06:58 (UTC+8)
出版者:	MDPI
摘要:	In this paper, we introduce a novel artificial intelligence technique with an attention mechanism for half-space electromagnetic imaging. A dielectric object in half-space is illuminated by TM (transverse magnetic) waves. Since measurements can only be made in the upper space, the measurement angle will be limited. As a result, we apply a back-propagation scheme (BPS) to generate an initial guessed image from the measured scattered fields for scatterer buried in the lower half-space. This process can effectively reduce the high nonlinearity of the inverse scattering problem. We further input the guessed images into the generative adversarial network (GAN) and the self-attention generative adversarial network (SAGAN), respectively, to compare the reconstruction performance. Numerical results prove that both SAGAN and GAN can reconstruct dielectric objects and the MNIST dataset under same measurement conditions. Our analysis also reveals that SAGAN is able to reconstruct electromagnetic images more accurately and efficiently than GAN.
關聯:	Sensors 24(7), 2322
DOI:	10.3390/s24072322
显示于类别:	[電機工程學系暨研究所] 期刊論文

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