Multi‐model fusion approach for electromagnetic inverse scattering problems

Abstract

In this paper, a novel deep learning (DL) approach is developed to solve the electromagnetic inverse scattering (EMIS) problems. Many challenges, such as ill‐posedness, high computational cost, and strong non‐linearity, are encountered when solving the EMIS problems. To surmount these difficulties, a multi‐model fusion convolutional neural network architecture is proposed, termed here as Amplitude‐Phase scheme. To the best of our knowledge, it is the first time that the multi‐model fusion DL approach is employed to solve the EMIS problems. Amplitude data and phase data of the measured scattering data are applied to train the proposed scheme. Furthermore, we compare APs with three different training schemes, including Amplitude‐Only scheme, and Phase‐Only scheme, and Complex‐Value scheme. The performance of the proposed DL schemes has been validated by numerical simulations. The results demonstrate that the proposed multi‐model fusion approach outperforms other DL schemes in terms of accuracy and is able to achieve a better performance in reconstructing homogeneous and heterogeneous scatterers.

A Two-Stream LSTM-Based Backscattering Model at L-Band and S- Band for Dry Soil Surfaces Under Large Roughness Conditions

“…All these outcomes serve as a novel path for solving electromagnetic scattering problems. However, until now most of the researches have focused on applying DL-based methods to develop an inversion algorithm to reproduce the parameters of interest [28]- [32], i.e., inverse problems, and few efforts have been reported to characterize the forward problems [33], [34]. Apparently, an accurate forward method can be guided to develop an effective retrieval algorithm.…”