Joint Estimation of NLOS Building Layout and Targets via Sparsity-Driven Approach
Joint Estimation of NLOS Building Layout and Targets via Sparsity-Driven Approach
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DOI：
10.1109/tgrs.2022.3182429
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发表时间：
2022
影响因子：
8.2
通讯作者：
Jiahui Chen;Yang Zhang;Shisheng Guo;G. Cui;Peilun Wu;Chao Jia;L. Kong
中科院分区：
文献类型：
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作者：
Jiahui Chen;Yang Zhang;Shisheng Guo;G. Cui;Peilun Wu;Chao Jia;L. Kong
非视线（NLOS）检测是一个持久的话题，因为它提供了一个强大的工具来监视视觉受阻的区域。目前，NLOS检测需要精确的建筑物布局先验知识，这限制了其在实际中的进一步应用。在这篇文章中，我们考虑的问题，联合估计的建筑物布局和目标位置在NLOS的情况下，利用多径返回。具体而言，首先，建筑布局被简化为未知参数的组合线性方程组。在此基础上，建立了多输入多输出（MIMO）雷达在多目标非视距情况下的参数化多径传播模型，并将其用于图像重建和布局估计问题。然后提出了一种形状重构组稀疏约束算法，并结合粒子群优化方法，同时重构未知布局和目标。与传统的基于压缩感知的方法相比，该方法综合了NLOS图像的基本结构特征和稀疏性先验，提高了解的稳定性。最后，所提出的方法的性能进行了验证，数值和实验结果。
Non-line-of-sight (NLOS) detection is an enduring topic as it provides a powerful tool to monitor visually blocked areas. Currently, NLOS detection requires precise prior knowledge of building layout, which limits its further applications in practice. In this article, we consider the problem of joint estimation of building layout and target location in the NLOS scenario by exploiting multipath returns. Specifically, first, the building layout is simplified into combined linear equations with unknown parameters. In this way, we establish a parametrized multipath propagation model in the multiple targets’ NLOS scenario for the multiple-input–multiple-output (MIMO) radar, which is used in the image reconstruction and layout estimation problem. Then, a shape remodeling group sparse constraint algorithm is proposed and combined with the particle swarm optimization method to simultaneously reconstruct the unknown layout and targets. Compared with the conventional compressed sensing-based methods, the proposed method integrates the basic structural characteristics and sparsity prior of the NLOS image to improve the stability of the solution. Finally, the performance of the proposed method is verified with numerical and experimental results.