一种基于Shapelet算法的指纹定位方法

doi:10.13190/j.jbupt.2019-223

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (4): 95-100.doi: 10.13190/j.jbupt.2019-223

一种基于Shapelet算法的指纹定位方法

常紫英¹, 王文涵¹, 李涛¹, 刘芬¹, 陈朋朋^1,2

1. 中国矿业大学计算机科学与技术学院, 徐州 221116;
2. 教育部矿山数字化工程研究中心, 徐州 221116

收稿日期:2019-10-10 发布日期:2020-08-15
通讯作者: 陈朋朋(1983-),男,教授,E-mail:chenp@cumt.edu.cn. E-mail:chenp@cumt.edu.cn
作者简介:常紫英(1995-),女,硕士生.
基金资助:
国家自然科学基金项目（51674255，51874302）

A Fingerprint Localization Method Based on Shapelet Algorithm

CHANG Zi-ying¹, WANG Wen-han¹, LI Tao¹, LIU Fen¹, CHEN Peng-peng^1,2

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China

Received:2019-10-10 Published:2020-08-15

摘要/Abstract

摘要： 信道状态信息（CSI）受时空影响较大，导致现有基于CSI的室内定位技术鲁棒性差.针对这一问题，提出了基于Shapelet算法的指纹定位方法.在训练阶段将CSI作为原始位置数据，通过3-σ异常值处理法和卡尔曼滤波对原始数据进行处理、修正；再使用Shapelet算法提取每个位置的指纹，并建立指纹库；最后使用指纹库构建Shapelet决策树，通过决策树分类实现较为精准的定位.通过与主成分分析算法以及k近邻算法的对比实验，结果表明，该方法在不同时间的定位精度较高，且能保持性能稳定，所需训练集更小.

关键词: 指纹定位, 信道状态信息, Shapelet算法, 决策树分类

Abstract: Due to large influence of time and space on channel state information (CSI),the existing CSI-based indoor positioning technology is poor in robustness.Aiming at this problem,a fingerprint positioning method based on Shapelet algorithm is proposed.In the training phase,CSI is taken as the original location data,and the original data is processed and corrected by the 3-σ anomaly value processing method and the Kalman filter;then the fingerprint of each location is extracted and the fingerprint database is established by using the Shapelet algorithm;Finally,the fingerprint database is used to construct the Shapelet decision tree,and the decision tree classification is used to achieve more accurate positioning.Compared with the principal components analysis algorithm and,k-nearest neighbor algorithm,It is shown that the method has higher positioning accuracy and stable performance at different times,and the training set is smaller.

Key words: fingerprint location, channel state information, Shapelet algorithm, decision tree classification

中图分类号:

TN92

常紫英, 王文涵, 李涛, 刘芬, 陈朋朋. 一种基于Shapelet算法的指纹定位方法[J]. 北京邮电大学学报, 2020, 43(4): 95-100.

CHANG Zi-ying, WANG Wen-han, LI Tao, LIU Fen, CHEN Peng-peng. A Fingerprint Localization Method Based on Shapelet Algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(4): 95-100.

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一种基于Shapelet算法的指纹定位方法

A Fingerprint Localization Method Based on Shapelet Algorithm

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