Received signal strength models for WLAN and BLE-based indoor positioning in multi-floor buildings

This paper investigates the similarities and differences of the signal strength fluctuations and positioning accuracy in indoor scenarios for three types of wireless area networks: two Wireless Local Area Networks (WLANs) at 2.4 GHz and 5 GHz frequency, respectively, and one Wireless Personal Area Network (WPAN), namely the Bluetooth Low Energy (BLE). Two path-loss models based on weighted centroids and non-negative least squares estimation are presented: one including a floor loss factor, and the other one ignoring the floor losses, and the three signal types are compared in terms of the path-loss parameters, channel fluctuations and positioning accuracy, namely the distance errors and floor detection probabilities. The comparison is done based on real-field measurement data collected from a university building in Tampere, Finland. It is shown that all these three signal types have similar shadowing variances and close path-loss parameter values, and that a path-loss model considering floor losses gives the best floor detection probability, but not necessarily the smallest distance error.

[1]  Zhengqing Yun,et al.  Propagation Measurement and Modeling for Indoor Stairwells at 2.4 and 5.8 GHz , 2014, IEEE Transactions on Antennas and Propagation.

[2]  S. Seidel,et al.  914 MHz path loss prediction models for indoor wireless communications in multifloored buildings , 1992 .

[3]  R. Faragher,et al.  An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications , 2014 .

[4]  Elena Simona Lohan,et al.  WLAN and RFID Propagation channels for hybrid indoor positioning , 2014, International Conference on Localization and GNSS 2014 (ICL-GNSS 2014).

[5]  Elena Simona Lohan,et al.  Deconvolution-based indoor localization with WLAN signals and unknown access point locations , 2013, 2013 International Conference on Localization and GNSS (ICL-GNSS).

[6]  Wenyu Liu,et al.  Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength , 2015, IEEE Transactions on Industrial Electronics.

[7]  Yong Ren,et al.  Does BTLE measure up against WiFi? A comparison of indoor location performance , 2014 .

[8]  Youngsu Cho,et al.  Improved Wi-Fi AP position estimation using regression based approach , 2013 .

[9]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[10]  Markku Renfors,et al.  Statistical path loss parameter estimation and positioning using RSS measurements in indoor wireless networks , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[11]  Simo Ali-L ¨ Oytty A Comparative Survey of WLAN Location Fingerprinting Methods , 2009 .

[12]  Yunhao Liu,et al.  Location, Localization, and Localizability , 2010, Journal of Computer Science and Technology.