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一种基于分布式压缩感知的矿井目标指纹数据库建立方法

田子建 贺方圆

田子建, 贺方圆. 一种基于分布式压缩感知的矿井目标指纹数据库建立方法[J]. 电子与信息学报, 2019, 41(10): 2450-2456. doi: 10.11999/JEIT180857
引用本文: 田子建, 贺方圆. 一种基于分布式压缩感知的矿井目标指纹数据库建立方法[J]. 电子与信息学报, 2019, 41(10): 2450-2456. doi: 10.11999/JEIT180857
Zijian TIAN, Fangyuan HE. A Method of Establishing Mine Target Fingerprint Database Based on Distributed Compressed Sensing[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2450-2456. doi: 10.11999/JEIT180857
Citation: Zijian TIAN, Fangyuan HE. A Method of Establishing Mine Target Fingerprint Database Based on Distributed Compressed Sensing[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2450-2456. doi: 10.11999/JEIT180857

一种基于分布式压缩感知的矿井目标指纹数据库建立方法

doi: 10.11999/JEIT180857
基金项目: 国家重点研发计划专项(2016YFC0801804),国家自然科学基金(51674269)
详细信息
    作者简介:

    田子建:男,1964年生,教授,研究方向为矿井定位与通信

    贺方圆:女,1987年生,博士生,研究方向为矿井定位与通信

    通讯作者:

    田子建 tzj@cumtb.edu.cn

  • 中图分类号: TD655

A Method of Establishing Mine Target Fingerprint Database Based on Distributed Compressed Sensing

Funds: The Special Program of the National Key Research and Development Plan of China (2016YFC0801804), The National Natural Science Foundation of China(51674269)
  • 摘要: 针对目前国内矿井目标定位精度低和定位实时性差的现况,该文提出一种基于分布式压缩感知原理构造指纹数据库的方法,该方法在离线阶段只需采集少量巷道中的指纹信息(参考节点ID信息、基于电磁波到达时间(TOA)的距离测量值和实际距离值),便可高概率重构矿井目标指纹数据库指纹信息,从而达到减少数据采集工作量和提高工作效率的目的。后续在线阶段,只需获得某时刻参考节点ID信息和目标节点被参考节点测得的实时TOA距离测量值,根据模式匹配方法可获得该时刻目标节点距离参考节点的待估距离值,保证了定位精度和定位实时性。在此基础上,提出一种改进的压缩采样修正匹配追踪算法(CoSaMMP)进行指纹信息重构,该算法利用折半法增大裁剪力度从而有效缩短重构数据时间。仿真结果表明所提算法的可行性及有效性。
  • 图  1  巷道目标节点位置指纹定位节点布置图

    图  2  定位流程图

    图  3  现场实测图

    图  4  采样方式

    图  5  目标节点距离${A_1}$的定位误差

    图  6  测量数和重构成功概率的对比图

    图  7  测量数和重构时间对比图

    表  1  指纹数据库指纹信号

    指纹信号指纹数据
    1${A_1}$,${A_1}$,$ ·\!·\!· $,${A_1}$($N$个${A_1}$)
    2${A_2}$,${A_2}$,$ ·\!·\!· $,${A_2}$($N$个${A_2}$)
    3${B_1}$,${B_1}$,$ ·\!·\!· $,${B_1}$($N$个${B_1}$)
    4${B_2}$,${B_2}$,$ ·\!·\!· $,${B_2}$($N$个${B_2}$)
    5${d_{11}}(p)$,${d_{21}}(p)$,$ ·\!·\!· $,${d_{N1}}(p)$
    6${d_{12}}(p)$,${d_{22}}(p)$,$ ·\!·\!· $,${d_{N2}}(p)$
    7${d_{13}}(p)$,${d_{23}}(p)$,$ ·\!·\!· $,${d_{N3}}(p)$
    8${d_{14}}(p)$,${d_{24}}(p)$,$ ·\!·\!· $,${d_{N4}}(p)$
    9$d\,'\!\!_{11}(p)$,$d\,'\!\!_{21}(p)$,$ ·\!·\!· $,$d\,'\!\!_{N1}(p)$
    10$d\,'\!\!_{12}(p)$,$d\,'\!\!_{22}(p)$,$ ·\!·\!· $,$d\,'\!\!_{N2}(p)$
    11$d\,'\!\!_{13}(p)$,$d\,'\!\!_{23}(p)$,$ ·\!·\!· $,$d\,'\!\!_{N3}(p)$
    12$d\,'\!\!_{14}(p)$,$d\,'\!\!_{24}(p)$,$ ·\!·\!· $,$d\,'\!\!_{N4}(p)$
    下载: 导出CSV

    表  2  各算法的时间复杂度

    算法时间复杂度(M<N)
    SVR-Kriging$O\left( {{N^3}} \right)$
    CoSaMPO(MN)
    CoSaMMP$\le$O(MN)
    ICoSaMMP(本文算法)$\le$O(MN)
    下载: 导出CSV

    表  3  本文算法各信号平均误差

    采样数l = 9l = 10l = 11l = 12
    Ml = 1000.981.060.900.96
    Ml = 1250.850.760.920.86
    下载: 导出CSV

    表  4  误差对比

    定位算法本文算法SVR-Kriging算法
    采样数Ml = 100Ml = 125Ml = 100
    最大误差2.371.851.90
    最小误差0.430.320.39
    平均误差0.980.850.92
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-09-03
  • 修回日期:  2019-05-14
  • 网络出版日期:  2019-05-24
  • 刊出日期:  2019-10-01

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