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Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

机译：基于压缩传感的无线传感器阵列网络采样与重构

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摘要

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework's superior performance, field experiment data from a prototype system is presented to validate the results.
机译：对于低功率无线系统，传输数据量是关键属性，它会影响能源成本和传输时间延迟。在本文中，我们介绍了压缩感测，以提出一种压缩采样和协作重建框架，该框架可实现无线传感器阵列网络的实时到达方向估计。在采样部分，利用随机压缩采样和1位采样来减少采样数据量，同时对硬件几乎没有额外的要求。在重建部分，通过利用来自同一阵列中节点的声信号的相似稀疏结构，提出了一种协同重建方法。仿真结果表明，所提出的框架可以达到与传统DoA方法相似的性能，同时所需的传输带宽不到15％。还将提出的框架与一些数据压缩算法进行了比较。虽然仿真结果显示了框架的出色性能，但仍提供了来自原型系统的现场实验数据以验证结果。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第9期|9641608.1-9641608.11|共11页
  • 作者

    Yin Ming; Yu Kai; Wang Zhi;

  • 作者单位

    Zhejiang Univ, Coll Control Sci & Engn, Hangzhou, Zhejiang, Peoples R China;

    Zhejiang Univ, Coll Control Sci & Engn, Hangzhou, Zhejiang, Peoples R China;

    Zhejiang Univ, Coll Control Sci & Engn, Hangzhou, Zhejiang, Peoples R China;

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