Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network

Weiwei Fan; Feng Zhou; Mingliang Tao; Xueru Bai; Pengshuai Rong; Shuang Yang; Tian Tian

Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network

2019

Radio Frequency Interference (RFI) is a key issue for Synthetic Aperture Radar (SAR) because it can seriously degrade the imaging quality, leading to the misinterpretation of the target scattering characteristics and hindering the subsequent image analysis. To address this issue, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on deep residual network (ResNet). First, the short-time Fourier transform (STFT) is used to characterize the interference-corrupted echo in the time&ndash:frequency domain. Then, the interference detection model is built by a classical deep convolutional neural network (DCNN) framework to identify whether there is an interference component in the echo. Furthermore, the time&ndash:frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time&ndash:frequency Fourier transform (ISTFT) is utilized to transform the time&ndash:frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulated and measured SAR data with strip mode and terrain observation by progressive scans (TOPS) mode. Moreover, in comparison with the notch filtering and the eigensubspace filtering, the proposed interference mitigation algorithm can improve the interference mitigation performance, while reducing the computation complexity.

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