Deep learning-based inversion methods for solving inverse scattering problems with phaseless data

Kuiwen Xu*, Liang Wu, Xiuzhu Ye, Xudong Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)

Abstract

Without phase information of the measured field data, the phaseless data inverse scattering problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full data ISPs (FD-ISPs). In this article, we propose a learning-based inversion approach in the frame of the U-net convolutional neural network (CNN) to quantitatively image unknown scatterers located in homogeneous background from the amplitude-only measured total field (also denoted PD). Three training schemes with different inputs to the U-net CNN are proposed and compared, i.e., the direct inversion scheme (DIS) with phaseless total field data, retrieval dominant induced currents by the Levenberg–Marquardt (LM) method (PD-DICs), and PD with contrast source inversion (PD-CSI) scheme. We also demonstrate the setup of training data and compare the performance of the three schemes using both numerical and experimental tests. It is found that the proposed PD-CSI and PD-DICs perform better in terms of accuracy, generalization ability, and robustness compared with DIS. PD-CSI has the strongest capability to tackle with PD-ISPs, which outperforms the PD-DICs and DIS.

Original languageEnglish
Article number9107447
Pages (from-to)7457-7470
Number of pages14
JournalIEEE Transactions on Antennas and Propagation
Volume68
Issue number11
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Convolutional neural network (CNN)
  • Inverse scattering problems (ISPs)
  • Phaseless data (PD)

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