Abstract
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–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–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 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.
Highlights
Synthetic Aperture Radar (SAR) has the advantages of full-time, all weather, long range, wide-swath, and high-resolution imaging, which plays a very important role in the fields of remote sensing, reconnaissance, space surveillance, and situational awareness [1,2,3,4,5,6,7]
Qualitative and quantitative metrics are utilized to evaluate the performance of different interference mitigation algorithms
The echo recovered by applying the interference mitigation network (IMN) was basically consistent with the original narrow-band interference (NBI)-free echo, which illustrates the effectiveness of the IMN
Summary
Synthetic Aperture Radar (SAR) has the advantages of full-time, all weather, long range, wide-swath, and high-resolution imaging, which plays a very important role in the fields of remote sensing, reconnaissance, space surveillance, and situational awareness [1,2,3,4,5,6,7]. Range-spectrum notch filtering is a simple but efficient method for interference mitigation It has been utilized in Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR ) [8] and Experimental airborne Synthetic Aperture Radar (E-SAR) systems [19]. The contribution of this paper can be summarized as follows: 1) An interference detection algorithm based on DCNN is proposed, which can effectively extract the time–frequency characteristics of NBI and WBI. It outperforms the state-of-the-art approaches by using a classical convolutional neural network architecture of VGG-16; 2) An interference mitigation algorithm based on ResNet is proposed.
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