室内BLE/MEMS跨楼层融合定位算法

doi:10.11959/j.issn.1000-436x.2017076

通信学报 ›› 2017, Vol. 38 ›› Issue (5): 1-10.doi: 10.11959/j.issn.1000-436x.2017076

• 学术论文 • 下一篇

室内BLE/MEMS跨楼层融合定位算法

周牧^1,²,王斌¹,田增山¹,张千坤¹

¹ 重庆邮电大学移动通信技术重庆市重点实验室，重庆 400065
² 天津师范大学天津市无线移动通信与无线电能传输重点实验室，天津 300387

修回日期:2017-02-22 出版日期:2017-05-01 发布日期:2017-05-28
作者简介:周牧（1984-），男，重庆人，博士，重庆邮电大学教授，主要研究方向为无线定位技术、机器学习与人工智能、凸优化理论。|王斌（1991-），男，重庆人，重庆邮电大学硕士生，主要研究方向为指纹定位技术、传感器定位技术、融合定位技术。|田增山（1968-），男，河南固始人，博士，重庆邮电大学教授，主要研究方向为蜂窝网无线定位系统、个人通信、GPS 精密定位和姿态测量、数据压缩和数据融合。|张千坤（1992-），男，河南固始人，重庆邮电大学硕士生，主要研究方向为室内定位技术。
基金资助:
国家自然科学基金资助项目(61301126);国家自然科学基金资助项目(61471077);长江学者和创新团队发展计划基金资助项目(IRT1299);重庆市科委重点实验室专项经费基金资助项目;重庆市基础与前沿研究计划基金资助项目（重点）(cstc2015jcyjBX0065);重庆市高校优秀成果转化基金资助项目(KJZH17117)

Indoor BLE and MEMS based multi-floor fusion positioning algorithm

Mu ZHOU^1,²,Bin WANG¹,Zeng-shan TIAN¹,Qian-kun ZHANG¹

¹ Chongqing Key Lab of Mobile Communications Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
² Tianjin Key Laboratory of Wireless Mobile Communication and Radio Power Transmission,Tianjin Normal University,Tianjin 300387,China

Revised:2017-02-22 Online:2017-05-01 Published:2017-05-28
Supported by:
The National Natural Science Foundation of China(61301126);The National Natural Science Foundation of China(61471077);Program for Changjiang Scholars and Innovative Research Team in University(IRT1299);Special Fund of Chongqing Key Laboratory (CSTC);Fundamental and Frontier Research Project of Chongqing (Key Project)(cstc2015jcyjBX0065);University Outstanding Achievement Transformation Project of Chongqing(KJZH17117)

摘要/Abstract

摘要：

提出一种基于微机电系统（MEMS,micro electro mechanical system）传感器与低功耗蓝牙(BLE,bluetooth low energy)数据融合的室内 BLE/MEMS 跨楼层定位算法。首先利用仿射传播聚类、离群点检测和接收信号强度（RSSI,received signal strength indicator）滤波算法对指纹库进行去噪，然后采用扩展卡尔曼滤波器，并根据抗差M估计方法对二维目标位置进行最优估计，最后基于气压计输出和地理位置信息实现对目标的高度估计。实验结果表明，该系统在室内环境下能够达到水平和垂直定位均方根误差小于0.7 m和0.35 m的跨楼层融合定位。

关键词: 室内定位, 跨楼层, 数据融合, 低功耗蓝牙, 微机电系统

Abstract:

Based on the data fusion from micro electro mechanical system (MEMS) sensors and low-power bluetooth (BLE),an indoor BLE and MEMS based multi-floor positioning algorithm was proposed.First of all,the affinity propagation clustering,outlier detection and received signal strength indicator (RSSI) filtering algorithms were applied to denoise the fingerprint database.Second,by using the extended Kalman filter,the robust M estimation algorithm was used to perform the optimal estimation of the two-dimensional target position.Finally,the barometer output and geographical position information was considered to realize the height estimation of the target.The experimental results show that the proposed system is able to achieve the horizontal and vertical positioning errors lower than 0.7 m and 0.35 m respectively in multi-floor fusion positioning.

Key words: indoor positioning, multi-floor,data fusion, BLE, MEMS

中图分类号:

TP391

周牧,王斌,田增山,张千坤. 室内BLE/MEMS跨楼层融合定位算法[J]. 通信学报, 2017, 38(5): 1-10.

Mu ZHOU,Bin WANG,Zeng-shan TIAN,Qian-kun ZHANG. Indoor BLE and MEMS based multi-floor fusion positioning algorithm[J]. Journal on Communications, 2017, 38(5): 1-10.

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分位值	融合定位/m	MEMS定位/m	BLE定位/m
50%	≤0.60	≤3.80	≤2.29
70%	≤0.77	≤4.48	≤3.75
90%	≤1.03	≤5.26	≤8.41

室内BLE/MEMS跨楼层融合定位算法

Indoor BLE and MEMS based multi-floor fusion positioning algorithm

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