基于LFM干扰稀疏参数估计的SAR图像射频干扰去除
RFI Removal from SAR Imagery via Sparse Parametric Estimation of LFM Interferences
AI总结：
本文提出用多个LFM分量混合建模SAR图像中的射频干扰，并通过稀疏参数估计结合二维频谱分析陷波滤波实现RFI抑制，在Sentinel-1数据上验证了其优于现有方法。
摘要：
星载合成孔径雷达（SAR）面临的挑战之一是对SAR图像中的射频干扰（RFI）伪影进行建模与抑制。线性调频（LFM）信号已被广泛用于表征SAR中的雷达干扰。本文提出一种新的信号模型，将聚焦SAR图像域中的RFI近似为多个LFM分量的混合。利用离散化LFM字典对LFM干扰进行稀疏参数化表示，有效估计每个LFM分量的方位向和距离向调频（FM）率。随后，在最近提出的RFI抑制框架内，通过LFM聚焦和谱域陷波滤波，使用二维频谱分析（2-D SPECAN）算法对该方法进行测试[1]。在Sentinel-1单视复数图像上的实验研究表明，所提出的LFM模型和稀疏参数估计方案优于现有的RFI去除方法。
英文摘要：
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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