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Joint Down-Range and Cross-Range RFI Suppression in Ultra-Wideband SAR
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2020-09-22 , DOI: 10.1109/tgrs.2020.3017485 Sonia Joy , Lam H. Nguyen , Trac D. Tran
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2020-09-22 , DOI: 10.1109/tgrs.2020.3017485 Sonia Joy , Lam H. Nguyen , Trac D. Tran
Radio frequency interference (RFI) is a critical problem for ultra-wideband synthetic aperture radars (UWB SAR) because the VHF/UHF band used by them is shared by other systems as well. A number of solutions have been proposed over the years. Recently, sparsity and low-rank estimation-based solutions were shown to perform better than traditional methods such as adaptive notch filtering. These algorithms model the SAR signal to be sparse and the RFI to be either sparse or low-ranked in nature and solve an optimization problem to estimate the SAR signal and RFI simultaneously. Algorithms in this class share the common characteristic that the SAR signal sparsity is captured by modeling each data vector as a linear combination of shifted SAR pulses. This data model addresses the structure of SAR signals in the down-range direction, but the inter-aperture cross-range structure, i.e., the fact that SAR signals add coherently across the cross-range, has been completely ignored. In this work, we incorporate this “global” 2-D structure of the SAR data into the RFI mitigation problem. Two algorithms are proposed: 1) (2-D) sparse SAR and sparse RFI estimation and 2) (2-D) sparse SAR and low-rank RFI estimation. The experimental results demonstrate that the 2-D model does a much better job of capturing the sparsity of SAR and the 2-D algorithms consistently perform better than the “local” 1-D algorithms. The level of improvement rises significantly in challenging cases—when the noise level and/or the number of RFI bands increases. Experiments are conducted extensively on simulated data sets as well as real SAR and RFI data sets collected by the U.S. Army Research Laboratory (ARL) to validate the proposed framework.
中文翻译:
超宽带SAR中的联合下限和跨范围RFI抑制
射频干扰(RFI)对于超宽带合成孔径雷达(UWB SAR)来说是一个关键问题,因为它们使用的VHF / UHF频段也被其他系统共享。这些年来,已经提出了许多解决方案。近来,稀疏性和基于低秩估计的解决方案表现出比传统方法(如自适应陷波滤波)更好的性能。这些算法本质上对SAR信号稀疏建模,RFI稀疏建模或低秩建模,并解决了优化问题,以便同时估算SAR信号和RFI。此类算法的共同特征是,通过将每个数据向量建模为移位SAR脉冲的线性组合来捕获SAR信号稀疏性。该数据模型解决了SAR信号在下行方向上的结构,但是光圈间跨范围结构,即SAR信号在整个跨范围内相干相加的事实,已被完全忽略。在这项工作中,我们将SAR数据的这种“全局”二维结构纳入RFI缓解问题中。提出了两种算法:1)(2-D)稀疏SAR和稀疏RFI估计; 2)(2-D)稀疏SAR和低秩RFI估计。实验结果表明,二维模型在捕获SAR稀疏性方面做得更好,并且二维算法始终比“局部”一维算法表现更好。在具有挑战性的情况下,当噪声水平和/或RFI频段数量增加时,改进水平会显着提高。对模拟数据集以及美国收集的实际SAR和RFI数据集进行了广泛的实验
更新日期:2020-09-22
中文翻译:
超宽带SAR中的联合下限和跨范围RFI抑制
射频干扰(RFI)对于超宽带合成孔径雷达(UWB SAR)来说是一个关键问题,因为它们使用的VHF / UHF频段也被其他系统共享。这些年来,已经提出了许多解决方案。近来,稀疏性和基于低秩估计的解决方案表现出比传统方法(如自适应陷波滤波)更好的性能。这些算法本质上对SAR信号稀疏建模,RFI稀疏建模或低秩建模,并解决了优化问题,以便同时估算SAR信号和RFI。此类算法的共同特征是,通过将每个数据向量建模为移位SAR脉冲的线性组合来捕获SAR信号稀疏性。该数据模型解决了SAR信号在下行方向上的结构,但是光圈间跨范围结构,即SAR信号在整个跨范围内相干相加的事实,已被完全忽略。在这项工作中,我们将SAR数据的这种“全局”二维结构纳入RFI缓解问题中。提出了两种算法:1)(2-D)稀疏SAR和稀疏RFI估计; 2)(2-D)稀疏SAR和低秩RFI估计。实验结果表明,二维模型在捕获SAR稀疏性方面做得更好,并且二维算法始终比“局部”一维算法表现更好。在具有挑战性的情况下,当噪声水平和/或RFI频段数量增加时,改进水平会显着提高。对模拟数据集以及美国收集的实际SAR和RFI数据集进行了广泛的实验