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Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

机译:无线传感器网络中基于LU分解的移动信标稀疏定位

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摘要

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.
机译:节点定位是无线传感器网络的核心。可以通过功能强大的信标解决该问题,这些信标配备有全球定位系统设备以了解其位置信息。在本文中,我们提出了一种基于LU分解的带有移动信标的新型稀疏定位方法。我们的方案首先通过建立稀疏定位模型将节点定位问题转化为1-稀疏向量恢复问题。然后,采用LU分解预处理来解决测量矩阵不满足约束等轴测特性的问题。以后,可以通过压缩感测精确地恢复1稀疏矢量。最后,由于1稀疏向量是近似稀疏的,因此引入了加权中心线方案以精确定位节点。仿真和分析表明,与MAP + GC,MAP-M和MAP-M&N方案相比,该方案具有更好的定位性能和对移动信标的较低要求。另外,障碍物和DOI对新方案影响不大,在低信噪比下具有很大的定位性能,因此该方案是健壮的。

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  • 年度 2015
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  • 正文语种 {"code":"en","name":"English","id":9}
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