Fast 3-D Electromagnetic Full-Wave Inversion of Dielectric Anisotropic Objects Based on ResU-Net Enhanced by Variational Born Iterative Method

Fei, Junjie; Chen, Yanjin; Zhong, Miao; Han, Feng

doi:10.1109/tap.2021.3138551

IEEE Trans. Antennas Propagat.

2022

DOI: 10.1109/tap.2021.3138551

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Fast 3-D Electromagnetic Full-Wave Inversion of Dielectric Anisotropic Objects Based on ResU-Net Enhanced by Variational Born Iterative Method

Junjie Fei

Yanjin Chen

Miao Zhong

et al.

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Cited by 4 publications

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Classification with electromagnetic waves

Simsek,

Manyam

2024

IET Microwaves Antenna & Prop

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The integration of neural networks and machine learning techniques has ushered in a revolution in various fields, including electromagnetic inversion, geophysical exploration, and microwave imaging. While these techniques have significantly improved image reconstruction and the resolution of complex inverse scattering problems, this paper explores a different question: Can near‐field electromagnetic waves be harnessed for object classification? To answer this question, we first create a dataset based on the MNIST dataset, where we transform the grayscale pixel values into relative electrical permittivity values to form scatterers and calculate the electromagnetic waves scattered from these objects using a 2D electromagnetic finite‐difference frequency‐domain solver. Then, we train various machine learning models with this dataset to classify the objects. When we compare the classification accuracy and efficiency of these models, we observe that the neural networks outperform others, achieving a 90% classification accuracy solely from the data without a need for projecting the input data into a latent space. The impacts of the training dataset size, the number of antennas, and the location of antennas on the accuracy and time spent during training are also investigated. These results demonstrate the potential for classifying objects with near‐field electromagnetic waves in a simple setup and lay the groundwork for further research in this exciting direction.

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Classification with electromagnetic waves

Simsek,

Manyam

2024

IET Microwaves Antenna & Prop

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Deep Injective Prior for Inverse Scattering

Khorashadizadeh,

Eskandari

et al. 2023

IEEE Trans. Antennas Propagat.

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In electromagnetic inverse scattering, the goal is to reconstruct object permittivity using scattered waves. While deep learning has shown promise as an alternative to iterative solvers, it is primarily used in supervised frameworks which are sensitive to distribution drift of the scattered fields, common in practice. Moreover, these methods typically provide a single estimate of the permittivity pattern, which may be inadequate or misleading due to noise and the ill-posedness of the problem. In this paper, we propose a data-driven framework for inverse scattering based on deep generative models. Our approach learns a low-dimensional manifold as a regularizer for recovering target permittivities. Unlike supervised methods that necessitate both scattered fields and target permittivities, our method only requires the target permittivities for training; it can then be used with any experimental setup. We also introduce a Bayesian framework for approximating the posterior distribution of the target permittivity, enabling multiple estimates and uncertainty quantification. Extensive experiments with synthetic and experimental data demonstrate that our framework outperforms traditional iterative solvers, particularly for strong scatterers, while achieving comparable reconstruction quality to state-of-theart supervised learning methods like the U-Net. I. INTRODUCTIONE LECTROMAGNETIC inverse scattering is the problem of determining the electromagnetic properties of unknown objects from how they scatter incident fields. This nondestructive technique finds applications in various fields, such as early detection of breast cancer [1], mineral prospecting [2], detecting defects and cracks inside objects [3], imaging through the walls [4] and remote sensing [5].

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Hybrid Microwave Imaging of 3-D Objects Using LSM and BIM Aided by a CNN U-Net

Han

Zhong

Fei

2022

IEEE Trans. Geosci. Remote Sensing

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Fast 3-D Electromagnetic Full-Wave Inversion of Dielectric Anisotropic Objects Based on ResU-Net Enhanced by Variational Born Iterative Method

Cited by 4 publications

References 53 publications

Classification with electromagnetic waves

Classification with electromagnetic waves

Deep Injective Prior for Inverse Scattering

Hybrid Microwave Imaging of 3-D Objects Using LSM and BIM Aided by a CNN U-Net

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