Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones

作     者：Imran Ashraf Soojung Hur Yousaf Bin Zikria Yongwan Park 

作者机构：Department of Information and Communication EngineeringYeungnam UniversityGyeongsan-si38541Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第8期

页      面：2597-2620页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 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] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2016-0-00313) supervised by the IITP(Institute for Information&communication Technology Promotion) supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science,ICT and Future Planning(2017R1E1A1A01074345) 

主　　题：Indoor localization magnetic field data long short term memory network data normalization gated recurrent unit network deep learning 

摘      要：Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data *** use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field *** the same vein,smaller lengths of magnetic field data decrease the localization accuracy *** current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the *** to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this *** of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG *** are performed during different times of the day to analyze the impact of time *** indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization *** comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.