Compressive sensing based target counting and localization exploiting joint sparsity
Compressive sensing based target counting and localization exploiting joint sparsity
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DOI：
10.1109/icassp.2016.7472274
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
E. Lagunas;S. Sharma;S. Chatzinotas;B. Ottersten
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文献类型：
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作者：
E. Lagunas;S. Sharma;S. Chatzinotas;B. Ottersten
无线传感器网络中的一个基本问题是对多个目标进行准确的计数和定位。在这种情况下，已经有越来越多的兴趣在文献中使用压缩感知（CS）为基础的技术，通过利用空间分布的目标在监测区域内的稀疏性。然而，大多数现有的工作旨在利用单测量矢量（SMV）模型的目标计数和定位。在本文中，我们考虑的问题，计数和定位多个目标利用联合稀疏特性的多测量向量（MMV）模型。此外，传统的MMV公式，其中相同的测量矩阵用于所有传感器是无效的，在实际的时变无线环境中。为了克服这个问题，我们将MMV问题重新表述为传统的SMV，其中MMV被矢量化。随后，我们提出了一种新的重建算法，它不需要先验知识的稀疏性水平，不像大多数现有的基于CS的方法。最后，我们评估所提出的算法的性能，并证明了所提出的MMV方法的优越性，其SMV对应的目标计数和定位精度。
One of the fundamental issues in Wireless Sensor Networks (WSN) is to count and localize multiple targets accurately. In this context, there has been an increasing interest in the literature in using Compressive Sensing (CS) based techniques by exploiting the sparse nature of spatially distributed targets within the monitored area. However, most existing works aim to count and localize the targets utilizing a Single Measurement Vector (SMV) model. In this paper, we consider the problem of counting and localizing multiple targets exploiting the joint sparsity feature of a Multiple Measurement Vector (MMV) model. Furthermore, the conventional MMV formulation in which the same measurement matrix is used for all sensors is not valid any more in practical time-varying wireless environments. To overcome this issue, we reformulate the MMV problem into a conventional SMV in which MMVs are vectorized. Subsequently, we propose a novel reconstruction algorithm which does not need the prior knowledge of the sparsity level unlike the most existing CS-based approaches. Finally, we evaluate the performance of the proposed algorithm and demonstrate the superiority of the proposed MMV approach over its SMV counterpart in terms of target counting and localization accuracies.