一種基于分布式壓縮感知的礦井目標指紋數(shù)據(jù)庫建立方法

田子建; 賀方圓

doi:10.11999/JEIT180857

一種基于分布式壓縮感知的礦井目標指紋數(shù)據(jù)庫建立方法

doi: 10.11999/JEIT180857

田子建^,,
賀方圓

中國礦業(yè)大學(北京)機電與信息工程學院 ??北京 ??100083

基金項目: 國家重點研發(fā)計劃專項(2016YFC0801804)，國家自然科學基金(51674269)

詳細信息

作者簡介:
田子建：男，1964年生，教授，研究方向為礦井定位與通信

賀方圓：女，1987年生，博士生，研究方向為礦井定位與通信

通訊作者:
田子建　tzj@cumtb.edu.cn

中圖分類號: TD655
計量
- 文章訪問數(shù): 2947
- HTML全文瀏覽量: 902
- PDF下載量: 64
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-09-03
- 修回日期: 2019-05-14
- 網(wǎng)絡出版日期: 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: 針對目前國內礦井目標定位精度低和定位實時性差的現(xiàn)況，該文提出一種基于分布式壓縮感知原理構造指紋數(shù)據(jù)庫的方法，該方法在離線階段只需采集少量巷道中的指紋信息(參考節(jié)點ID信息、基于電磁波到達時間(TOA)的距離測量值和實際距離值)，便可高概率重構礦井目標指紋數(shù)據(jù)庫指紋信息，從而達到減少數(shù)據(jù)采集工作量和提高工作效率的目的。后續(xù)在線階段，只需獲得某時刻參考節(jié)點ID信息和目標節(jié)點被參考節(jié)點測得的實時TOA距離測量值，根據(jù)模式匹配方法可獲得該時刻目標節(jié)點距離參考節(jié)點的待估距離值，保證了定位精度和定位實時性。在此基礎上，提出一種改進的壓縮采樣修正匹配追蹤算法(CoSaMMP)進行指紋信息重構，該算法利用折半法增大裁剪力度從而有效縮短重構數(shù)據(jù)時間。仿真結果表明所提算法的可行性及有效性。
- 分布式壓縮感知 /
- 指紋數(shù)據(jù)庫 /
- 壓縮采樣修正匹配追蹤 /
- TOA測距
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

圖 1 巷道目標節(jié)點位置指紋定位節(jié)點布置圖

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圖 2 定位流程圖

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圖 3 現(xiàn)場實測圖

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圖 4 采樣方式

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圖 5 目標節(jié)點距離${A_1}$的定位誤差

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圖 6 測量數(shù)和重構成功概率的對比圖

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圖 7 測量數(shù)和重構時間對比圖

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表 1 指紋數(shù)據(jù)庫指紋信號

指紋信號	指紋數(shù)據(jù)
1	${A_1}$,${A_1}$,$ ·\!·\!· $,${A_1}$($N$個${A_1}$)
2	${A_2}$,${A_2}$,$ ·\!·\!· $,${A_2}$($N$個${A_2}$)
3	${B_1}$,${B_1}$,$ ·\!·\!· $,${B_1}$($N$個${B_1}$)
4	${B_2}$,${B_2}$,$ ·\!·\!· $,${B_2}$($N$個${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 各算法的時間復雜度

算法	時間復雜度(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 本文算法各信號平均誤差

采樣數(shù)	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 誤差對比

定位算法	本文算法		SVR-Kriging算法
采樣數(shù)	M_l = 100	M_l = 125	M_l = 100
最大誤差	2.37	1.85	1.90
最小誤差	0.43	0.32	0.39
平均誤差	0.98	0.85	0.92

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