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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Improving RPCA-Based Clutter Suppression in GPR Detection of Antipersonnel Mines
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Improving RPCA-Based Clutter Suppression in GPR Detection of Antipersonnel Mines

机译：在基于GPR的杀伤人员地雷检测中改进基于RPCA的杂波抑制

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摘要

Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment.
机译：由于地面杂波污染常常会掩盖APM的响应，因此使用探地雷达（GPR）探测浅埋的杀伤人员地雷（APM）是一项艰巨的任务。在这封信中，提出了一种将迁移成像与低秩和稀疏表示方法相结合的新方法来抑制杂波并提取目标图像。所提出的方法首先聚焦并通过迁移成像增强了目标响应。然后，由于聚焦的目标响应和混乱分别构成了记录数据的稀疏分量和低秩分量，因此，最近提出的鲁棒主分量分析（RPCA）可以应用于记录数据以分离目标响应（稀疏的组件）（杂乱无章的组件）。进行了实际GPR系统的数值模拟和实验。结果证明了所提方法在提高信杂比和检索目标几何信息方面的有效性，从而可以在繁杂的杂波环境中更好地识别APM。

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