Interference Mitigation for Synthetic Aperture Radar Using Deep Learning

In this paper, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on the deep residual network (ResNet). First, the short-time Fourier transform (STFT) is utilized to characterize the interference-corrupted echo in the time-frequency domain. Then, the interference detection model is built by the classical convolutional neural network (CNN) framework to identify whether the echoes exist interference signal component. Furthermore, the time-frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time-frequency Fourier transform (ISTFT) is utilized to transform the time-frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulation and measured SAR data of the terrain observation by progressive scans (TOPS) mode. Moreover, the performance comparison with the notch filtering and eigensubspace filtering demonstrates the superiority of the proposed interference mitigation algorithm.