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[1]邵小强,赵轩,聂馨超,等.1种抑制巷道信号NLOS的矿井RSSI高精度定位算法*[J].中国安全生产科学技术,2021,17(9):18-24.[doi:10.11731/j.issn.1673-193x.2021.09.003]
　SHAO Xiaoqiang,ZHAO Xuan,NIE Xinchao,et al.A RSSI high-precision localization algorithm for suppressing roadway signals NLOS of mine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(9):18-24.[doi:10.11731/j.issn.1673-193x.2021.09.003]
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1种抑制巷道信号NLOS的矿井RSSI高精度定位算法*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年9期
页码:
18-24
栏目:
学术论著
出版日期:
2021-09-30

文章信息/Info

Title:
A RSSI high-precision localization algorithm for suppressing roadway signals NLOS of mine
文章编号:
1673-193X(2021)-09-0018-07
作者:
邵小强; 赵轩; 聂馨超; 郭德锋; 郑润洋; 卫晋阳; 赵宇
(西安科技大学 电气与控制工程学院，陕西 西安 710054)
Author(s):
SHAO Xiaoqiang;  ZHAO Xuan;  NIE Xinchao;  GUO Defeng;  ZHENG Runyang;  WEI Jinyang;  ZHAO Yu
(School of Electric and Control Engineering,Xi’an University of Science and Technology,Xi’an Shaanxi 710054,China)
关键词:
矿井定位; RSSI; 高斯滤波; 卡尔曼滤波; 指纹定位; NLOS
Keywords:
mine localization;  received signal strength indication (RSSI);  Gaussian filter;  Kalman filter;  fingerprint localization;  non line of sight (NLOS)
分类号:
X936;TD76
DOI:
10.11731/j.issn.1673-193x.2021.09.003
文献标志码:
A
摘要:
在RSSI（Received Signal Strength Indication）测距定位技术中，为抑制巷道信号NLOS（Non Line of Sight）传输对定位结果的影响，提出信号指纹定位和几何优化算法。在离线阶段利用高斯滤波最大值加权法和最小二乘法建立符合矿井巷道环境的无线信号测距模型，设计改进卡尔曼滤波器平滑处理离线信号值，抑制巷道信号NLOS传输带来的影响，建立离线信号指纹库；在线定位阶段，利用加权K最近邻法（WKNN）将定位目标接收到的信号值与指纹库中的信号值进行匹配，将匹配到的最优信号值参与测距定位计算，最后通过几何优化算法将定位结果归一化处理，使其符合一维定位分布。结果表明：所提算法的平均定位误差为0.9 m，相比于高斯滤波最大值加权法、经典卡尔曼滤波指纹定位算法和改进卡尔曼滤波指纹定位方法，其平均误差分别减小2.36，1.17，0.35 m。所提算法能够有效抑制巷道信号NLOS传输对RSSI测距定位的影响，可实现RSSI方法在矿井NLOS环境中的有效应用。
Abstract:
In order to suppress the influence of the non line of sight (NLOS) transmission of roadway signals on the localization results in the ranging localization technology of received signal strength indication (RSSI),the signal fingerprint localization and geometric optimization algorithms were put forward.In the off-line stage,the maximal weighted method and least square method of Gaussian filter were used to establish the wireless signal ranging model in line with the mine roadway environment,then an improved Kalman filter was designed to smoothly process the off-line signal values for suppressing the influence of NLOS transmission of roadway signals,and a fingerprint database of off-line signals was established.In the online localization stage,the weighted K-nearest neighbor method (WKNN) was used to match the signal values received by the localization target with the signal values in the fingerprint database,and the matched optimal signal value was involved in the calculation of ranging localization.Finally,the localization results were normalized through the geometric optimization algorithm to make them conform to the one-dimensional localization distribution.The results showed that the average localization error of the proposed algorithm was 0.9 m,which was 2.36 m,1.17 m and 0.35 m less than that of the Gaussian filter maximum weighted method,the classical Kalman filter fingerprint localization algorithm and the improved Kalman filter fingerprint localization method,respectively.The proposed algorithm can effectively suppress the influence of NLOS transmission of roadway signals on the RSSI ranging localization,and realize the effective application of RSSI method in the NLOS environment of mine.

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备注/Memo

备注/Memo:
收稿日期： 2021-04-02；网络首发日期： 2021-08-04
* 基金项目： 国家自然科学基金项目（61603295）；陕西省自然科学基础研究计划项目(2018JM6003)
作者简介： 邵小强，博士，副教授，主要研究方向为智慧矿山物联网建设、人员定位及路径规划。
通信作者： 赵轩，硕士研究生，主要研究方向为矿井人员定位。
更新日期/Last Update: 2021-10-02