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首页> 外文期刊>International Journal of Distributed Sensor Networks >Multiple Target Localization in WSNs Based on Compressive Sensing Using Deterministic Sensing Matrices
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Multiple Target Localization in WSNs Based on Compressive Sensing Using Deterministic Sensing Matrices

机译：基于确定性感知矩阵的压缩感知的无线传感器网络多目标定位

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摘要

Accurate and low-cost localization of multiple targets or nodes is one of fundamental and challenging technical issues in wireless sensor networks (WSNs). Furthermore, compressive sensing allows that a sparse signal can be reconstructed from few measurements, and choosing a suitable sensing matrix is also important. For this purpose, random sensing matrices have been studied, while a few researches on deterministic sensing matrices have been considered. In this paper, we use compressive sensing for multiple target localization in WSNs. We formulate multiple target locations as a sparse matrix in the discrete time domain. Then, we exploit received signal strength information to recover noisy measurements, while utilizing deterministic sensing matrices and greedy algorithm to locate each target. The proposal approach reduces the number of measurements in localization process, takes low-cost, and maintains the accuracy as compared to the conventional approach which is noncompressive sensing. Further simulation shows that the proposed approach is practical in use, while being favorably comparable to the existing random sensing matrices in reconstruction performance. This cooperation between the compressive sensing using deterministic sensing matrices and multiple target localization provides a new point of view in WSN localization.
机译：多个目标或节点的精确且低成本的定位是无线传感器网络（WSN）中基本且具有挑战性的技术问题之一。此外，压缩感测允许从很少的测量中重建稀疏信号，并且选择合适的感测矩阵也很重要。为此目的，已经研究了随机感测矩阵，同时考虑了关于确定性感测矩阵的一些研究。在本文中，我们将压缩感知用于WSN中的多个目标定位。我们将多个目标位置公式化为离散时域中的稀疏矩阵。然后，我们利用接收到的信号强度信息来恢复噪声测量，同时利用确定性传感矩阵和贪婪算法来定位每个目标。与非压缩感测的传统方法相比，该提议的方法减少了定位过程中的测量数量，降低了成本，并保持了准确性。进一步的仿真表明，所提出的方法在使用中是实用的，同时在重建性能方面可以与现有的随机传感矩阵相媲美。使用确定性感测矩阵的压缩感测与多目标定位之间的这种协作为WSN定位提供了新的观点。

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