A Res-FCN for Electromagnetic Inversion of High Contrast Scatterers at an Arbitrary Frequency Within a Wide Frequency Band

TL;DR

This paper introduces a Res-FCN deep learning model capable of performing microwave inversion of high contrast scatterers at any frequency within a broad band, enhancing generalizability over traditional frequency-specific methods.

Contribution

The work develops a novel Res-FCN architecture that combines Res-Net and FCN for frequency-independent microwave inversion, addressing limitations of existing methods.

Findings

Achieves accurate inversion at arbitrary frequencies within a wide band.

Demonstrates robustness against noise in numerical examples.

Outperforms traditional methods in high contrast scatterer scenarios.

Abstract

Many successful machine learning methods have been developed for microwave inversion problems. However, so far, their inversion has been performed only at the specifically trained frequencies. To make the machine-learning-based inversion method more generalizability for realistic engineering applications, this work proposes a residual fully convolutional network (Res-FCN) to perform microwave inversion of high contrast scatterers at an arbitrary frequency within a wide frequency band. The proposed Res-FCN combines the advantages of the Res-Net and the fully convolutional network (FCN). Res-FCN consists of an encoder and a decoder: the encoder is employed to extract high-dimensional features from the measured scattered field through the residual frameworks, while the decoder is employed to map from the high-dimensional features extracted by the encoder to the electrical parameter distribution in the inversion region by the up-sample layer and the residual frameworks. Five numerical examples verify that the proposed Res-FCN can achieve good performance in the 2-D microwave inversion problem for high contrast scatterers with anti-noise ability at an arbitrary frequency point within a wide frequency band.