RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods
RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods
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DOI：
10.1109/tgrs.2018.2880758
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发表时间：
2019-06
影响因子：
8.2
通讯作者：
Jiaying Ren;Tianyi Zhang;Jian Li;L. Nguyen;P. Stoica
中科院分区：
文献类型：
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作者：
Jiaying Ren;Tianyi Zhang;Jian Li;L. Nguyen;P. Stoica
射频干扰（RFI）会严重影响超宽带（UWB）雷达的成像性能和目标检测性能。本文制定了适当的数据模型，并提出了有效的RFI缓解新方法。我们首先应用单次快照稀疏迭代基于协方差的估计（SPICE）算法从每个脉冲重复间隔的RFI缓解的数据，并讨论连接SPICE的$l_{1}$ -惩罚最小绝对偏差（$l_{1}$ -PLAD）的方法。然后，我们设计了一个改进的群SPICE算法，并证明了它等价于$l_{1，2}$ -PLAD方法的一个特例。修改后的组SPICE算法可以应用于来自相干处理间隔的数据，以有效地减轻RFI。单次快拍SPICE和改进的组SPICE方法同时利用RFI频谱和UWB雷达目标回波的稀疏特性。与现有的基于稀疏性的RFI抑制方法，如鲁棒主成分分析算法，所提出的方法是超参数，因此更容易在实际应用中使用。此外，SPICE方法的快速实现被认为是利用单快拍和多快拍协方差矩阵的特殊结构。最后，从应用SPICE方法模拟数据以及美国陆军研究实验室合成孔径雷达系统收集的测量数据所得到的结果，以证明所提出的方法的有效性。
Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the $l_{1}$ -penalized least absolute deviation ( $l_{1}$ -PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the $l_{1,2}$ -PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.