A Novel Indoor Positioning Framework

被引:5
作者
Chen, Ming-Chih [1 ]
Cheng, Yin-Ting [1 ]
Chen, Ru-Wei [1 ]
机构
[1] Natl Kaohsiung Univ Sci & Technol, Dept Elect Engn, First Campus, Kaohsiung 82445, Taiwan
来源
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES | 2022年 / 130卷 / 03期
关键词
Indoor positioning; Bluetooth protocol; artificial intelligence;
D O I
10.32604/cmes.2022.015636
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Current positioning systems are primarily based on the Global Positioning System (GPS). Although the GPS is accurate within 10 m, it is mainly used for outdoor positioning services (Location-Based Service; LBS). However, since satellite signals cannot penetrate buildings, indoor positioning has always been a blind spot for satellite signals. As indoor positioning applications are extensive with high commercial values, they have created a competitive niche in the market. Existing indoor positioning technologies are unable to achieve less than 10 cm accuracy except for the Ultra Wide Band (UWB) technology. On the other hand, the Bluetooth protocol achieves an accuracy of 1 to 2 m. In this work, we use Bluetooth wireless signals to build a novel indoor positioning framework to avoid the high manufacturing costs involved in the UWB technology. The Bluetooth signals are combined with the results from artificial intelligence algorithms to improve accuracy. During laboratory indoor location tracking, the accuracy rate is 96%, which provides effective indoor tracking for the movement of people.
引用
收藏
页码:1459 / 1477
页数:19
相关论文
共 15 条
[1]   MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System [J].
AL-Khaleefa, Ahmed Salih ;
Ahmad, Mohd Riduan ;
Isa, Azmi Awang Md ;
AL-Saffar, Ahmed ;
Esa, Mona Riza Mohd ;
Malik, Reza Firsandaya .
INFORMATION, 2019, 10 (04)
[2]  
Alkandari M., 2017, WORKSH REC TRENDS TE, P1
[3]  
Cui YL, 2018, CHIN AUTOM CONGR, P3283, DOI 10.1109/CAC.2018.8623304
[4]   Connecting the physical world with pervasive networks [J].
Estrin, Deborah ;
Culler, David ;
Pister, Kris ;
Sukhatme, Gaurav .
IEEE Pervasive Computing, 2002, 1 (01) :59-69
[5]   NEURAL NETWORKS AND PHYSICAL SYSTEMS WITH EMERGENT COLLECTIVE COMPUTATIONAL ABILITIES [J].
HOPFIELD, JJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1982, 79 (08) :2554-2558
[6]  
Jordan M., 1986, Tech. Rep.
[7]  
Kim J, 2016, INT CONF UBIQ FUTUR, P468, DOI 10.1109/ICUFN.2016.7537073
[8]  
Kosba AE, 2012, IEEE WCNC, P3284, DOI 10.1109/WCNC.2012.6214375
[9]  
Ma W., 2015, IEEE INT C SMART CIT, P1
[10]  
Oguntala G, 2017, PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE INTERNET TECHNOLOGIES AND APPLICATIONS (ITA), P212, DOI 10.1109/ITECHA.2017.8101941