Compressive Sensing-Based Multipath Exploitation for Stationary and Moving Indoor Target Localization
Compressive Sensing-Based Multipath Exploitation for Stationary and Moving Indoor Target Localization
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DOI：
10.1109/jstsp.2015.2464177
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发表时间：
2015-12-01
影响因子：
7.5
通讯作者：
Zoubir, Abdelhak M.
中科院分区：
文献类型：
--
作者：
Leigsnering, Michael;Ahmad, Fauzia;Zoubir, Abdelhak M.
基于压缩感知（CS）的多径探测已成功应用于穿墙雷达成像（TWRI）中的静止室内场景。使用显著减少的数据的益处对于移动目标也是期望的。因此，我们把CS为基础的多径开发的非平稳目标域，并能够同时处理运动和静止目标。通常，多径传播对图像质量具有不利影响。然而，通过使用适当的建模，多路径可以被用于一个人的优势。在本文中，我们适用于CS下的内壁散射的静止和运动目标。假设房间的几何形状的知识，我们开发了一种有效的方法，解决了逆问题的联合定位和速度估计的目标在室内多径环境中从几个测量。我们还提出了一个计算成本低的计划，首先使用稀疏重建定位的目标，随后估计的速度矢量。所提出的方法的有效性证明使用模拟和实验数据。
Compressive sensing (CS)-based multipath exploitation has been successfully applied to stationary indoor scenes in through-the-wall radar imaging (TWRI). The benefits of using significantly reduced data are also desirable for moving targets. Hence, we bring CS based multipath exploitation to the non-stationary target domain and are able to treat moving and stationary targets simultaneously. In general, multipath propagation has adverse effects on the image quality. However, by using proper modeling, multipath can be used to one's advantage. In this paper, we apply CS to both stationary and moving targets under interior wall scatterings. Assuming knowledge of the room geometry, we develop an effective method that solves the inverse problem of joint localization and velocity estimation of the targets in an indoor multipath environment from a few measurements. We also propose a computationally inexpensive scheme that first locates the targets using sparse reconstruction and subsequently estimates the velocity vectors. Effectiveness of the proposed methods is demonstrated using both simulated and experimental data.