基于信道状态信息的矿难人员检测研究

doi:10.13190/j.jbupt.2018-223

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (4): 96-101.doi: 10.13190/j.jbupt.2018-223

基于信道状态信息的矿难人员检测研究

孙朝宇, 高守婉, 杨旭, 陈朋朋, 牛强

中国矿业大学计算机科学与技术学院, 江苏徐州 221116

收稿日期:2018-09-11 出版日期:2019-08-28 发布日期:2019-08-26
作者简介:孙朝宇(1996-),男,硕士生,E-mail:suncy@cumt.edu.cn;陈朋朋(1983-),男,教授,博士生导师.
基金资助:
国家自然科学基金项目（51774282）；江苏省自然科学基金项目（BK20160274）

Research on Personnel Detection for Mine Accident Based on Channel State Information

SUN Chao-yu, GAO Shou-wan, YANG Xu, CHEN Peng-peng, NIU Qiang

School of Computer Science and Technology, China University of Mining and Technology, Jiangsu Xuzhou 221116, China

Received:2018-09-11 Online:2019-08-28 Published:2019-08-26

摘要/Abstract

摘要： 针对传统矿难救援方法设备昂贵、探距较短、误报率高等问题，提出了一种基于信道状态信息的矿难人员检测方法.首先，提出了基于高斯混合模型前景检测方法，以判断被困人员的活跃程度；其次，根据信道状态信息周期性变化，利用自相关函数捕捉人员呼吸频率，以检测非活跃人员；最后在多种实验参数下对所提方法进行性能评估.结果证明，所提方法具有较高的准确度和鲁棒性，平均准确率可达90%.

关键词: 信道状态信息, 高斯混合模型, 前景检测, 人员检测

Abstract: Aiming at the problems of high cost, short detection distance and high false alarm rate in traditional rescue methods, a new method for personnel detection based on channel state information is proposed. First, the state detection module determines the degree of the activity according to the Gaussian mixed distribution. Second, the breath detection module uses periodicity of the influence of human on channel state information to extract breathing, and to find the breathing frequency of the people who remain inactive. Finally, we evaluated the system in a variety of parameters. The experimental result verifies that the system has high accuracy and robustness,and the average recognition rate can reach 90%.

Key words: channel state information, Gaussian mixed distribution, foreground detection, personnel detection

中图分类号:

TP391

孙朝宇, 高守婉, 杨旭, 陈朋朋, 牛强. 基于信道状态信息的矿难人员检测研究[J]. 北京邮电大学学报, 2019, 42(4): 96-101.

SUN Chao-yu, GAO Shou-wan, YANG Xu, CHEN Peng-peng, NIU Qiang. Research on Personnel Detection for Mine Accident Based on Channel State Information[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(4): 96-101.

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基于信道状态信息的矿难人员检测研究

Research on Personnel Detection for Mine Accident Based on Channel State Information

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