基于CSI-XGBoost的高精度WiFi室内定位算法

doi:10.13700/j.bh.1001-5965.2018.0268

北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (12): 2536-2544.doi: 10.13700/j.bh.1001-5965.2018.0268

基于CSI-XGBoost的高精度WiFi室内定位算法

张玄黎, 修春娣, 王延昭, 杨东凯

北京航空航天大学电子信息工程学院, 北京 100083

收稿日期:2018-05-11 修回日期:2018-06-15 出版日期:2018-12-20 发布日期:2018-12-28
通讯作者: 修春娣 E-mail:xcd@buaa.edu.cn
作者简介:张玄黎,女,硕士研究生。主要研究方向:基于CSI的WiFi室内定位指纹匹配算法研究;修春娣,女,讲师。主要研究方向:无线通信和室内定位。
基金资助:
北航金华北斗技术成果转化及产业化基金项目（多传感器融合智能室内定位系统研发）（BARI1701）

High-precision WiFi indoor localization algorithm based on CSI-XGBoost

ZHANG Xuanli, XIU Chundi, WANG Yanzhao, YANG Dongkai

School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Received:2018-05-11 Revised:2018-06-15 Online:2018-12-20 Published:2018-12-28
Supported by:
Beihang Jinhua Beidou Technology Achievement Transformation and Industrialization Fund Project (Multi-sensor Fusion Intelligent Indoor Positioning System R&D)(BARI1701)

摘要/Abstract

摘要： 考虑到室内环境的复杂性和多径效应对WiFi指纹定位性能的影响从Intel 5300无线网卡中提取信道状态信息（CSI），利用修正后的CSI幅值和相位信息作为指纹特征，使用极限梯度提升（XGBoost）算法构建高精度指纹库，实现分米级的高精度室内定位。进一步通过实测数据分析了采样间隔、室内视距（LOS）和非视距（NLOS）环境、缺失值和数据维度等因素对所提算法定位性能的影响。实际室内环境下的实验结果表明，本文算法受NLOS影响较小，对室内复杂环境有很强的鲁棒性；此外，该算法能够很好地处理高维稀疏数据，解决CSI指纹特征的"误匹配"问题，且对缺失数据不敏感，定位准确度优于90%。

关键词: 室内定位, 信道状态信息(CSI), 指纹匹配, 极限梯度提升(XGBoost), 相位延拓

Abstract: Considering the influence of complex indoor environment and multi-path effects on the WiFi fingerprint positioning performance, this paper extracts channel state information (CSI) from the Intel 5300 wireless network card and utilizes the modified CSI amplitude and phase information as fingerprint features. A high-precision fingerprint database was built using the extreme gradient boosting (XGBoost) algorithm to achieve indoor positioning at a decimeter level. Experiments in the actual indoor environment have been conducted to evaluate the effects of sampling interval,line of sight (LOS) and non line of sight (NLOS), missing values, and data dimensions on the localization performance of the proposed method. The results of real indoor experiment show that the proposed CSI-XGBoost method is less affected by NLOS and robust to complex indoor environments. In addition, this method can handle high-dimensional sparse data well and solve the mismatching problem of CSI fingerprinting. Moreover, this method is insensitive to missing data, with localization accuracy of better than 90%.

Key words: indoor localization, channel state information (CSI), fingerprint matching, extreme gradient boosting (XGBoost), phase extension

中图分类号:

张玄黎, 修春娣, 王延昭, 杨东凯. 基于CSI-XGBoost的高精度WiFi室内定位算法[J]. 北京航空航天大学学报, 2018, 44(12): 2536-2544.

ZHANG Xuanli, XIU Chundi, WANG Yanzhao, YANG Dongkai. High-precision WiFi indoor localization algorithm based on CSI-XGBoost[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2018, 44(12): 2536-2544.

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基于CSI-XGBoost的高精度WiFi室内定位算法

High-precision WiFi indoor localization algorithm based on CSI-XGBoost

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