煤矿井下区间分段视距节点合作定位算法
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摘要
针对煤矿井下长距离定位时节点信号波动大、非视距路径信号衰减严重造成定位精度低问题,提出一种区间分段式视距节点合作定位算法。该算法利用学习向量量化聚类将长距离信号传输区间自定义分段,利用分段阈值选择未知节点所属区间;把已定位出结果的未知节点视为其他未知节点的虚拟参考节点,实现所有节点信息相互交流,在节点筛选思想下,利用信道状态信息,克服多径效应来寻找视距路径节点,将近距离区间内的已定位视距路径节点代替远距离区间内的参考节点,减少远距离参考节点的使用。结果表明,与传统未分段、未寻找视距路径节点合作的算法相比,定位误差只有1.5m,精度提高率达到85%。
To overcome the problem of serious signal fluctuation and Non-line-of-sight signal attenuation with long distance positioning in a coal mine,a segmentation node cooperative localization algorithm is proposed.By using the learning vector quantization clustering to segment the long distance transmission interval,the threshold is used to select the range for an unknown node.We think of the unknown node that has been located as the virtual reference node of other unknown nodes,so that all node information can communicate with each other.In the idea of node screening,the multi-path effects are overcome and line-ofsight nodes are searched by channel state information.The reference nodes in the long range are replaced by the optimally located line-of-sight nodes in the close range.Results show that compared with the traditional unsegmented and unfinished line-of-sight path nodes,the positioning error is only 1.5 mand the accuracy improvement rate is 85%.
To overcome the problem of serious signal fluctuation and Non-line-of-sight signal attenuation with long distance positioning in a coal mine,a segmentation node cooperative localization algorithm is proposed.By using the learning vector quantization clustering to segment the long distance transmission interval,the threshold is used to select the range for an unknown node.We think of the unknown node that has been located as the virtual reference node of other unknown nodes,so that all node information can communicate with each other.In the idea of node screening,the multi-path effects are overcome and line-ofsight nodes are searched by channel state information.The reference nodes in the long range are replaced by the optimally located line-of-sight nodes in the close range.Results show that compared with the traditional unsegmented and unfinished line-of-sight path nodes,the positioning error is only 1.5 mand the accuracy improvement rate is 85%.
引文
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[18]魏振春,吕增威,杨成,等.基于RSS正确性判定的矿井机车无线定位方法[J].仪器仪表学报,2014,35(1):178-184.WEI Zhenchun,LV Zengwei,YANG Cheng,et al.Wireless Positioning Method Based on Correctnee Judgment of Receiver Signal Strength or Mine Locomotive[J].Chinese Journal of Scientific Instrument,2014,35(1):178-184.
[2]邢天璋,王举,陈晓江,等.WSN中异构数据协同的目标定位方法[J].西安电子科技大学学报,2014,41(5):197-202.XING Tianzhang,WANG Ju,CHEN Xiaojiang,et al.Heterogeneous Data Synergistic Location Method in the WSN[J].Journal of Xidian University,2014,41(5):197-202.
[3]REHMANY U,TARIQ M,KHAN O U.Improved Object Localization Using Accurate Distance Estimation in Wireless Multimedia Sensor Networks[J].Plos One,2015,10(11):e0141558.
[4]胡青松,张申,吴立新,等.矿井动目标定位:挑战、现状与趋势[J].煤炭学报,2016,41(5):1059-1068.HU Qingsong,ZHANG Shen,WU Lixin,et al.Mine Moving Target Location:Challenges,Current Situation and Trends[J].Journal of China Coal Society,2016,41(5):1059-1068.
[5]邢天璋,谢彬彬,杨康,等.一种利用无线信号的被动式多目标定位系统[J].西安电子科技大学学报,2018,45(1):93-98.XING Tianzhang,XIE Binbin,YANG Kang,et al.Passive Localization for Multi-targets via Wireless Signal Processing[J].Journal of Xidian University,2018,45(1):93-98.
[6]蔡雄.基于CSI的室内Wi Fi定位技术研究[D].西安:西安电子科技大学,2015.
[7]TZUR A,WOOL A.Direction Finding of Rogue Wi-Fi Access Points Using an Off-the-shelf MIMO-OFDM Receiver[J].Physical Communication,2015,17(C):149-164.
[8]江禹生,冯砚毫,管芳,等.无线传感网非测距三维节点定位算法[J].西安电子科技大学学报,2012,39(5):140-147.JIANG Yusheng,FENG Yanhao,GUAN Fang,et al.Range-free Three-dimensional Node Location Algorithm for the Wireless Sensor Network[J].Journal of Xidian University,2012,39(5):140-147.
[9]阔永红,周文文,陈健.避免非视距影响的蒙特卡罗移动节点定位方法[J].西安电子科技大学学报,2014,41(1):6-12.KUO Yonghong,ZHOU Wenwen,CHEN Jian.Monte Carlo Mobile Node Localization for NLOS Environment[J].Journal of Xidian University,2014,41(1):6-12.
[10]SHEN J,WANG A,WANG C,et al.A RFID Based Localization Algorithm Applying Trilateration for Wireless Sensor Networks[J].Journal of Internet Technology,2017,18(5):1167-1175.
[11]XU J,HE J,ZHANG Y,et al.A Distance-based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks[J].International Journal of Distributed Sensor Networks,2016(8):1.
[12]BRO R,de JONG S.A Fast Non-negativity Constrained Least Squares Algorithm[J].Journal of Chemometrics,2015,11(5):393-401.
[13]刘晓阳,李宗伟,方轲,等.基于距离约束的井下目标定位方法[J].煤炭学报,2014,39(4):789-794.LIU Xiaoyang,LI Zongwei,FANG Ke,et al.Location Method of Downhole Target Based on Distance Constraint[J].Journal of China Coal Society,2014,39(4):789-794.
[14]赵季红,谢志勇,曲桦,等.一种简化鲁棒的传感网络节点三维估计算法[J].西安电子科技大学学报,2018,45(5):108-114.ZHAO Jihong,XIE Zhiyong,QU Hua,et al.Simplified and Robust Algorithm for Three-dimensional Estimation of Nodes in Sensor Networks[J].Journal of Xidian University,2018,45(5):108-114.
[15]ZHOU Z,YANG Z,WU C,et al.LiFi:Line-of-sight Identification with WiFi[C]//Proceedings of the 2014IEEEConference on Computer Communications.Piscataway:IEEE,2014:2688-2696.
[16]WU C,YANG Z,ZHOU Z,et al.PhaseU:Real-time LOS Identification with WiFi[C]//Proceedings of the 2015IEEEConference on Computer Communications.Piscataway:IEEE,2015:2038-2046.
[17]李论,张著洪,丁恩杰,等.基于RSSI的煤矿巷道高精度定位算法研究[J].中国矿业大学学报,2017,46(1):183-191.LI Lun,ZHANG Zhuhong,DING Enjie,et al.Research on High Precision Positioning Algorithm of Coal Mine Roadway Based on RSSI[J].Journal of China University of Mining and Technology,2017,46(1):183-191.
[18]魏振春,吕增威,杨成,等.基于RSS正确性判定的矿井机车无线定位方法[J].仪器仪表学报,2014,35(1):178-184.WEI Zhenchun,LV Zengwei,YANG Cheng,et al.Wireless Positioning Method Based on Correctnee Judgment of Receiver Signal Strength or Mine Locomotive[J].Chinese Journal of Scientific Instrument,2014,35(1):178-184.