An investigation of different Wi-Fi signal behaviours and their effects on indoor positioning accuracy

被引:7
作者
Ilci, V [1 ]
Gulal, E. [2 ]
Alkan, R. M. [1 ,3 ]
机构
[1] Hitit Univ, Vocat Sch Tech Sci, TR-19169 Corum, Turkey
[2] Yildiz Tech Univ, Dept Geodesy & Photogrammetry, TR-34220 Istanbul, Turkey
[3] Istanbul Tech Univ, Dept Geomat Engn, TR-34469 Istanbul, Turkey
关键词
Wi-Fi; 2.4; GHz; 5; Bilateration; Trilateration; Weighted iterative nonlinear least square; Extended Kalman filter; Indoor positioning; LOCALIZATION; BILATERATION;
D O I
10.1080/00396265.2017.1292672
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In recent years, in addition to 2.4 GHz Wi-Fi signals, 5 GHz signals have been introduced making it possible to transmit data at both IEEE 802.11n and 802.11ac standards. Therefore, researchers have increasingly focused on developing indoor positioning applications based on Wi-Fi dual band. This study was conducted in two stages. In the first stage, we investigated the behaviours of 2.4 and 5 GHz Wi-Fi signals by collecting received signal strength (RSS) values from access points and determined the relationship between RSS and distance for each signal frequency using a curve fitting technique. Furthermore, we comparatively analysed signal fluctuations and their effects on positioning accuracy. In the second part of the study, we compared the positioning accuracy of four algorithms; namely, bilateration, trilateration, weighted iterative non-linear least square and extended Kalman filter using 2.4 and 5 GHz Wi-Fi signals. The experimental results revealed that the 5 GHz signals were more stable and had better positioning accuracy than the 2.4 GHz signals. Concerning the positioning algorithms, bilateration had the best positioning accuracy at both frequencies.
引用
收藏
页码:404 / 411
页数:8
相关论文
共 36 条
[21]  
Mautz R., 2012, INDOOR POSITIONING T
[22]   Adaptive Empirical Path Loss Prediction Models for Indoor WLAN [J].
Naik, Udaykumar ;
Bapat, Vishram N. .
WIRELESS PERSONAL COMMUNICATIONS, 2014, 79 (02) :1003-1016
[23]  
Seybold JS, 2005, INTRODUCTION TO RF PROPAGATION, P1, DOI 10.1002/0471743690
[24]   ANALYSIS AND STATUS QUO OF SMARTPHONE-BASED INDOOR LOCALIZATION SYSTEMS [J].
Subbu, Kalyan P. ;
Zhang, Chi ;
Luo, Jun ;
Vasilakos, Athanasios V. .
IEEE WIRELESS COMMUNICATIONS, 2014, 21 (04) :106-112
[25]  
Talvitie J, 2015, IEEE GLOBE WORK
[26]   Distance-Based Interpolation and Extrapolation Methods for RSS-Based Localization With Indoor Wireless Signals [J].
Talvitie, Jukka ;
Renfors, Markku ;
Lohan, Elena Simona .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2015, 64 (04) :1340-1353
[27]   Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization [J].
Tarrio, Paula ;
Bernardos, Ana M. ;
Casar, Jose R. .
SENSORS, 2011, 11 (09) :8569-8592
[28]  
Tarrío P, 2008, 2008 IEEE INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATION SYSTEMS (ISWCS 2008), P358
[29]   Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength [J].
Wang, Bang ;
Zhou, Shengliang ;
Liu, Wenyu ;
Mo, Yijun .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (01) :572-582
[30]   A study on wireless sensor network based indoor positioning systems for context-aware applications [J].
Wang, Jing ;
Prasad, R. Venkatesha ;
An, Xueli ;
Niemegeers, Ignas G. M. M. .
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2012, 12 (01) :53-70