A WiFi Fingerprint Based High-Adaptability Indoor Localization via Machine Learning

作     者：Jianzhe Xue Junyu Liu Min Sheng Yan Shi Jiandong Li Jianzhe Xue;Junyu Liu;Min Sheng;Yan Shi;Jiandong Li

作者机构：School of Telecommunication EngineeringXidian UniversityXi’an 710071China State Key Laboratory of Integrated Service NetworksXidian UniversityXi’an 710071China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2020年第17卷第7期

页      面：247-259页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the Natural Science Foundation of China(61725103,61701363,61931005,and 91638202) in part by Young Elite Scientists Sponsorship Program by CAST in part by the Key Industry Innovation Chain of Shaanxi under Grant 2017ZDCXL-GY-04-04 in part by Fundamental Research Funds for the Central Universities。 

主　　题：WiFi absolute error 

摘      要：For received signal strength(RSS)fingerprint based indoor localization approaches,the localization accuracy is significantly influenced by the RSS variance,device heterogeneity and environment complexity.In this work,we present a high-adaptability indoor localization(HAIL)approach,which leverages the advantages of both relative RSS values and absolute RSS values to achieve robustness and accuracy.Particularly,a backpropagation neural network(BPNN)is devised in HAIL to measure the fingerprints similarities based on absolute RSS values.With this aid,the characteristics of the applied area could be specially learned such that HAIL could be adaptive to different environments.The experiments demonstrate that HAIL achieves high localization accuracy with the average localization error of 0.87 m in the typical environments.Moreover,HAIL has the minimum amount of large errors and decreases the average localization error by about 30%~50%compared with the existing approaches.