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Microwave Imaging of Anisotropic Objects by Artificial Intelligence Technology.

Shu-Han Liao¹, Chien-Ching Chiu¹, Po-Hsiang Chen¹

¹Department of Electrical and Computer and Engineering, Tamkang University, New Taipei City 251301, Taiwan.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

Artificial intelligence, specifically a convolution neural network (CNN), successfully reconstructs microwave images of biaxial anisotropic objects. This method overcomes challenges posed by transverse electronic (TE) polarization, proving more effective than traditional schemes.

Keywords:

anisotropic objects artificial intelligence convolution neural network dielectric objects inverse scattering problem microwave imaging

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Area of Science:

Electromagnetics
Artificial Intelligence
Microwave Imaging

Background:

Biaxial anisotropic scatterers present complex challenges for microwave imaging due to differing dielectric constants.
Transverse electronic (TE) polarization waves encounter higher nonlinearity than transverse magnetic (TM) waves, complicating image reconstruction.
Existing methods struggle to accurately reconstruct microwave images from scattered field information of anisotropic objects.

Purpose of the Study:

To develop and validate an artificial intelligence-based approach for microwave imaging of biaxial anisotropic objects.
To address the complexities associated with TE polarization in microwave imaging.
To compare the efficacy of different initial image reconstruction schemes when used with a CNN.

Main Methods:

Utilized the dominant current scheme (DCS) and back-propagation scheme (BPS) for initial image estimation.
Applied a trained convolution neural network (CNN) to regenerate and refine microwave images.
Conducted numerical simulations to evaluate the CNN's performance and generalization ability.

Main Results:

The CNN demonstrated good generalization capabilities, even with limited training data.
The proposed CNN-based method successfully reconstructed microwave images of biaxial anisotropic objects.
Comparison showed the dominant current scheme (DCS) outperformed the back-propagation scheme (BPS) when integrated with the CNN.

Conclusions:

Convolution neural networks offer a promising solution for complex microwave imaging problems involving anisotropic objects.
The AI-driven approach effectively overcomes the high nonlinearity challenges of TE polarization.
The dominant current scheme (DCS) is a more suitable precursor for CNN-based microwave image reconstruction of anisotropic scatterers compared to BPS.