Radio Frequency Interference Suppression in SAR System Using Prior-Induced Deep Neural Network

Abstract

The existence of Radio Frequency (RF) Interference will cause an adverse effect on the interpretation of Synthetic Aperture Radar (SAR) images. There are various types of interference, and their pattens in images vary in different situations. Previous algorithms have disadvantages of low precision and large amount of computation. In this paper, we propose a prior-induced deep neural network. Based on the sparse and low-rank properties of interference signals in the time-frequency domain, an interference suppression network is designed to reconstruct useful signals. At the same time, a new loss function is designed, which integrates the sparse and low-rank properties with the training of network. The network combines the idea of semi-parametric interference suppression and the deep learning method, which can make good use of the characteristics of SAR echoes, making it more suitable for the field of signal processing and having a better effect. The proposed algorithm is applied to real SAR data with interference to validate its effect and efficiency.