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

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

田子建^,,
贺方圆

中国矿业大学(北京)机电与信息工程学院北京 100083

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
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- 被引次数: 23
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

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

Zijian TIAN^,,
Fangyuan HE

School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

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

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。
- 分布式压缩感知 /
- 指纹数据库 /
- 压缩采样修正匹配追踪 /
- TOA测距
Abstract: A method of establishing a fingerprint database, which is based on distributed compressed sensing, is proposed to improve the low positioning accuracy and poor real-time positioning that exist in the current mine target positioning in China. Using the method, the fingerprint information of mine target fingerprint database can be reconstructed with high probability by collecting only a few fingerprint information (reference node IDs, Time Of Arrival (TOA) measurements based on electromagnetic wave and actual distance values) in the roadway in the off-line stage. Therefore, the data collection workload can be reduced and the work efficiency can be improved as well. In the subsequent on-line stage, according to the pattern matching method, the estimated distance between the target node and the reference nodes at the certain time can be obtained only by getting the reference node IDs and the real-time TOA measurements measured by the reference nodes at a certain moment, which guarantees the positioning accuracy and positioning real-time performance. Based on this method, an improved Compressive Sampling Modifying Matching Pursuit (CoSaMMP) algorithm is proposed to reconstruct the fingerprint information. The algorithm can effectively shorten the reconstruction time by using the folding method to increase the cutting force. The simulation results show that the proposed algorithm is feasible and effective.
- Distributed compressed sensing /
- Fingerprint database /
- CompressiveSamplingModifyingMatchingPursuit (CoSaMMP) /
- TimeOfArrival (TOA) distance measurement

HTML全文

图 1 巷道目标节点位置指纹定位节点布置图

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图 2 定位流程图

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图 3 现场实测图

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图 4 采样方式

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图 5 目标节点距离 ${A_1}$ 的定位误差

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图 6 测量数和重构成功概率的对比图

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图 7 测量数和重构时间对比图

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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	$d\,'\!\!_{14}(p)$ , $d\,'\!\!_{24}(p)$ , $·\!·\!·$ , $d\,'\!\!_{N4}(p)$

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表 2 各算法的时间复杂度

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

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表 3 本文算法各信号平均误差

采样数	l = 9	l = 10	l = 11	l = 12
M_l = 100	0.98	1.06	0.90	0.96
M_l = 125	0.85	0.76	0.92	0.86

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表 4 误差对比

定位算法	本文算法		SVR-Kriging算法
采样数	M_l = 100	M_l = 125	M_l = 100
最大误差	2.37	1.85	1.90
最小误差	0.43	0.32	0.39
平均误差	0.98	0.85	0.92

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