Design and implementation of indoor positioning system based on iBeacon

With the rapid increase in data and multimedia services, demand for positioning has increased especially in complex indoor environment which often needs to determine the location information of the mobile terminal. There is a lack of accuracy and robustness in current indoor positioning system. This paper designs and implements an indoor positioning system based on iBeacon. We adopt Gaussian filter and Unscented Kalman filter method to robustly extract strong signals from iBeacon device. With the extracted signals, we compared them with-in database. The goal of this paper is to design and implement a mobile-based indoor location system which has the mobile applications with the Bluetooth Low Energy technology based on the iBeacon. Using a mobile terminal our system can show position results. Moreover, our system can run on both Android systems and IOS ones. Our method has better performance compared with WiFi method. The experimental results demonstrates that the error is only within 4 meters and our system can achieve accurate and robust positioning.

[1]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[2]  References , 1971 .

[3]  Lu Xiaochun,et al.  Design and Simulation of Indoor Positioning System Based on UWB , 2010, 2010 International Conference on E-Business and E-Government.

[4]  Luca Benini,et al.  Bluetooth indoor localization with multiple neural networks , 2010, IEEE 5th International Symposium on Wireless Pervasive Computing 2010.

[5]  Shuming Nie The processing and application of the urban geographical basic data , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[6]  Yuanzhu Chen,et al.  Indoor positioning of mobile devices with agile iBeacon deployment , 2015, 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE).

[7]  John K. Pollard,et al.  Position measurement using Bluetooth , 2006, IEEE Transactions on Consumer Electronics.

[8]  Henrique Marra Menegaz,et al.  A Systematization of the Unscented Kalman Filter Theory , 2015, IEEE Transactions on Automatic Control.

[9]  Takaya Yamazato,et al.  UWB positioning using known indoor features - environment comparison , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[10]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).