|本期目录/Table of Contents|

[1]余修武,范飞生,李睿,等.基于接收信号强度分区矿山无线定位算法[J].中国安全生产科学技术,2015,11(9):70-75.[doi:10.11731/j.issn.1673-193x.2015.09.011]
 YU Xiu-wu,FAN Fei-sheng,LI Rui,et al.Study on wireless positioning algorithm in mine based on received signal strength partition[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(9):70-75.[doi:10.11731/j.issn.1673-193x.2015.09.011]
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基于接收信号强度分区矿山无线定位算法
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
11
期数:
2015年9期
页码:
70-75
栏目:
职业安全卫生管理与技术
出版日期:
2015-09-30

文章信息/Info

Title:
Study on wireless positioning algorithm in mine based on received signal strength partition
作者:
余修武范飞生李睿江珊
(南华大学 环境保护与安全工程学院,湖南 衡阳 421001)
Author(s):
YU Xiu-wu FAN Fei-sheng LI Rui JIANG Shan
Environmental Protection and Safety Engineering Institute, University Of South China, Hengyang Hunan 421001, China)
关键词:
矿山节点间分区信号强度分级定位无线传感器网络
Keywords:
mine partition between nodes signal strength grading positioning wireless sensor network
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2015.09.011
文献标志码:
A
摘要:
针对井下工作人员的平时考勤以及发生井下事故时能够对受困人员进行及时有效的定位搜救,提出了一种接收信号强度分区(RSSP)定位算法。RSSP定位算法把井下相邻参考节点间划分为若干分区,根据井下人员(移动节点)所在巷道位置的接收信号及强度情况对其进行定位。此算法比较简单,功耗低。经过MATLAB模拟仿真,得出RSSP算法比经典的RSSI和RFID算法定位精度更高。
Abstract:
A positioning algorithm based on received signal strength partition (RSSP) was proposed for the daily checking attendance of underground workers and the timely and effective positioning rescue for stranded persons in case of underground accident. In the RSSP algorithm, the spacing between adjacent reference nodes is divided into several partitions, and the positions of underground workers (moving nodes) are determined according to the received signal and the corresponding strength in the roadway. The algorithm is relatively simple, with low power consumption. Through simulation by MATLAB, it showed that the positioning precision of RSSP algorithm was higher than that of traditional RSSI and RFID algorithm.

参考文献/References:

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相似文献/References:

[1]韩光胜,程健维,易光旺.国外矿山开采沉陷预计技术研究及应用概述[J].中国安全生产科学技术,2011,7(10):41.
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备注/Memo

备注/Memo:
湖南省科技厅重点研发项目(2015SK2005);江西省科技厅科技项目(20121BBG70065);江西省教育厅科技项目(GJJ12667);南华大学博士基金项目(2013XQD12);湖南省教育厅自然科学研究重点项目(15A161)
更新日期/Last Update: 2015-09-30