Compressive sensing (CS) applied to through-the-wall radar imaging (TWRI) exploits the group sparsity of a target scene in the presence of wall clutter and multipath from enclosed structures towards achieving high-resolution imaging with limited measurements. In this paper, we extend the CS-based TWRI to include the clustering structure property of the target within a hierarchical Bayesian framework. An extended structured spike-and-slab prior is imposed to statistically encourage spatially extended cluster structures of a target scene and model the signal group sparsity due to multipath propagation. The expectation propagation scheme is used for the approximate posterior inference. The proposed nonparametric Bayesian algorithm can achieve substantial improvements in terms of preserving a target cluster structure and suppressing isolated spurious false alarms compared to other state-of-the-art algorithms. Furthermore, it does not require prior information about the targets themselves, such as size, shape or number.

中文翻译：

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基于利用多路径和目标结构的贝叶斯压缩感知的穿墙雷达成像


应用于穿墙雷达成像 (TWRI) 的压缩感知 (CS) 利用目标场景在存在墙壁杂波和封闭结构多路径的情况下的群体稀疏性，以有限的测量实现高分辨率成像。在本文中，我们扩展了基于 CS 的 TWRI，以将目标的聚类结构属性包含在分层贝叶斯框架内。施加扩展的结构化尖峰和平板先验以在统计上鼓励目标场景的空间扩展集群结构，并对由于多路径传播导致的信号组稀疏性进行建模。期望传播方案用于近似后验推理。与其他最先进的算法相比，所提出的非参数贝叶斯算法可以在保留目标簇结构和抑制孤立的虚假误报方面实现实质性改进。此外，它不需要有关目标本身的先验信息，例如大小、形状或数量。