Through-the-wall radar imaging is a sensing technology that can be used by first responders to see through obscure barriers during search-and-rescue missions or deployed by law enforcement and military personnel to maintain situational awareness during tactical operations. However, the strong reflections from the front wall and other obstacles render the detection of stationary targets very difficult. In this article, a learning-based approach is proposed to mitigate the effect of the wall and background clutter. A sparse autoencoder with a low-rank projection is developed to mitigate the wall clutter and recover the target signal. The weights of the proposed autoencoder are determined by solving an augmented Lagrange multiplier optimization problem, and the regularization parameters are estimated using the Bayesian optimization technique. Experiments using real data from a stepped-frequency radar were conducted to illustrate its effectiveness for wall clutter removal. The results show that the proposed method achieves superior performance compared with the existing approaches.

中文翻译：

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使用具有低秩投影的稀疏自编码器在穿墙雷达成像中去除杂波

穿墙雷达成像是一种传感技术，急救人员可以使用它在搜救任务期间穿透模糊的障碍物，或者由执法人员和军事人员部署，以在战术行动期间保持态势感知。然而，前墙和其他障碍物的强烈反射使静止目标的检测变得非常困难。在本文中，提出了一种基于学习的方法来减轻墙壁和背景杂波的影响。开发了具有低秩投影的稀疏自编码器以减轻墙壁杂波并恢复目标信号。所提出的自动编码器的权重是通过解决一个增广拉格朗日乘数优化问题来确定的，并且正则化参数是使用贝叶斯优化技术估计的。使用来自步进频率雷达的真实数据进行了实验，以说明其去除墙壁杂波的有效性。结果表明，与现有方法相比，所提出的方法具有更好的性能。