Subspace-Based Distorted FDFD Iterative Method for Inverse Scattering

Wei, Teng-Fei; Wang, Xiaohua; Wang, Bing‐Zhong

doi:10.1109/lgrs.2022.3156896

Cited by 3 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2021, Zhao proposed a highly efficient inversion method that utilized the improved subspace-regularized distorted-Born iterative method and the multilevel Green's function interpolation method to solve 3D ISP [23]. In 2022, Wei proposed a subspace-based, distorted finite difference frequency domain-based iterative method to improve the efficiency of electromagnetic scattering inversion [33]. In 2023, Sun proposed cross-correlated contrast source inversion in homogeneous background media [40].…”

Section: Introductionmentioning

confidence: 99%

Microwave Imaging of Anisotropic Objects by Artificial Intelligence Technology

Liao,

Chiu,

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%