用稀疏参数化方法精准识别并去除雷达图像中的线性调频干扰。
RFI Removal from SAR Imagery via Sparse Parametric Estimation of LFM Interferences
- 将干扰建模为多个线性调频分量混合，通过离散字典实现高效参数估计。
- 在哨兵-1图像上验证，去干扰效果优于现有方法。
- 适合需要高精度雷达图像处理的研究者和工程师。
星载合成孔径雷达（SAR）面临的一个挑战是建模与消除雷达图像中的射频干扰（RFI）伪影。线性调频（LFM）信号常用于表征SAR中的雷达干扰。本文提出一种新信号模型，将RFI近似为聚焦SAR图像域中多个LFM分量的混合。利用离散化的LFM字典，通过稀疏参数化表示有效估计每个LFM分量的方位和距离频率调制率。该方法在最近提出的基于二维谱分析（2-D SPECAN）算法的RFI抑制框架中进行测试，通过LFM聚焦与频域陷波滤波实现干扰去除。对Sentinel-1单视复数图像的实验研究表明，所提出的LFM模型与稀疏参数估计方案优于现有RFI去除方法。
原文摘要 · Abstract (English)
One of the challenges in spaceborne synthetic aperture radar (SAR) is modeling and mitigating radio frequency interference (RFI) artifacts in SAR imagery. Linear frequency modulated (LFM) signals have been commonly used for characterizing the radar interferences in SAR. In this letter, we propose a new signal model that approximates RFI as a mixture of multiple LFM components in the focused SAR image domain. The azimuth and range frequency modulation (FM) rates for each LFM component are estimated effectively using a sparse parametric representation of LFM interferences with a discretized LFM dictionary. This approach is then tested within the recently developed RFI suppression framework using a 2-D SPECtral ANalysis (2-D SPECAN) algorithm through LFM focusing and notch filtering in the spectral domain [1]. Experimental studies on Sentinel-1 single-look complex images demonstrate that the proposed LFM model and sparse parametric estimation scheme outperforms existing RFI removal methods.
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