Low-Frequency Data Prediction With Iterative Learning for Highly Nonlinear Inverse Scattering Problems

Abstract

In this work, we present a deep-learning-based low-frequency (LF) data prediction scheme to solve the highly nonlinear inverse scattering problem (ISP) with strong scatterers. The nonlinearity of ISP is alleviated by introducing the LF components in full-wave inversion. In this scheme, a deep neural network (DNN) is trained to predict the absent LF scattered field data from the measured high-frequency (HF) data. Then, a frequency-hopping technique is applied to invert the predicted LF data and measured HF data, where the inverted LF model is served as an initial guess for the HF data inversion. In this way, the risk of HF data inversion getting trapped in local minima is largely reduced. Furthermore, an iterative training method is employed to continuously update the DNN based on the previous inverted model to predict more accurate LF data, thereby improving the reconstruction result. Both synthetic and experimental results are performed to verify the efficacy of this scheme.