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WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

机译：基于压缩感知的无线传感器网络微震信号分段压缩算法

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摘要

For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS) used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR) algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times.
机译：针对无线网络微震监测以及通信的压缩率低，通信能耗高的问题，针对微震信号的特点，结合传输过程中使用的压缩感知理论，提出一种分段压缩算法。该算法将被收集为数据分段的多个非零元素，通过减少分段内非零元素的组合数量来提高信号重建的准确性，同时利用压缩感测理论的特性来实现较高的信号的压缩率。实验结果表明，在量子混沌免疫克隆重构（Q-CSDR）算法的重构算法中，在信号稀疏度大于40的条件下，要以大于0.4的压缩比压缩信号，均方根误差小于0.01，可将网络寿命延长2倍。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第11期|187095.1-187095.9|共9页
  • 作者

    Liu Zhouzhou; Wang Fubao;

  • 作者单位

    Xian Aeronaut Univ, Xian 710077, Peoples R China.;

    Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China.;

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