Microwave Imaging of Anisotropic Objects by Artificial Intelligence Technology

doi:10.3390/s23218781

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 987654321/125846

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题名:	Microwave Imaging of Anisotropic Objects by Artificial Intelligence Technology
作者:	Liao, Shu-han;Chiu, Chien-ching;Chen, Po-hsiang
关键词:	microwave imaging;anisotropic objects;convolution neural network;dielectric objects;artificial intelligence;inverse scattering problem
日期:	2023-10-27
上传时间:	2024-08-05 12:05:28 (UTC+8)
出版者:	MDPI
摘要:	In this paper, we present the microwave imaging of anisotropic objects by artificial intelligence technology. Since the biaxial anisotropic scatterers have different dielectric constant components in different transverse directions, the problems faced by transverse electronic (TE) polarization waves are more complex than those of transverse magnetic (TM) polarization waves. In other words, measured scattered field information can scarcely reconstruct microwave images due to the high nonlinearity characteristic of TE polarization. Therefore, we first use the dominant current scheme (DCS) and the back-propagation scheme (BPS) to compute the initial guess image. We then apply a trained convolution neural network (CNN) to regenerate the microwave image. Numerical results show that the CNN possesses a good generalization ability under limited training data, which could be favorable to deploy in image processing. Finally, we compare DCS and BPS reconstruction images for anisotropic objects by the CNN and prove that DCS is better than BPS. In brief, successfully reconstructing biaxial anisotropic objects with a CNN is the contribution of this proposal.
關聯:	Sensors 23(21),　8781
DOI:	10.3390/s23218781
显示于类别:	[電機工程學系暨研究所] 期刊論文

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