MS in Electrical Engineering (M.S.E.E.)
Degree Granting Department
Ismail Uysal, Ph.D.
Jing Wang, Ph.D.
Nasir Ghani, Ph.D.
Artificial Neural Network, Lateration, LOS Channel, Maximum Likelihood Estimation, On-Off Keying, Regression
Passive ultra-high frequency (UHF) radio frequency identification (RFID) systems have gained immense popularity in recent years for their wide-scale industrial applications in inventory tracking and management. In this study, we explore the potential of passive RFID systems for indoor localization by developing a grid-based experimental framework using two standard and easily measurable performance metrics: received signal strength indicator (RSSI) and tag read count (TRC). We create scenarios imitating real life challenges such as placing metal objects and other RFID tags in two different read fields (symmetric and asymmetric) to analyze their impacts on location accuracy. We study the prediction potential of RSSI and TRC both independently and collaboratively. In the end, we demonstrate that both signal metrics can be used for localization with sufficient accuracy whereas the best performance is obtained when both metrics are used together for prediction on an artificial neural network especially for more challenging scenarios. Experimental results show an average error of as low as 0.286 (where consecutive grid distance is defined as unity) which satisfies the grid-based localization benchmark of less than 0.5.
Scholar Commons Citation
Jeevarathnam, Nanda Gopal, "Grid-Based RFID Indoor Localization Using Tag Read Count and Received Signal Strength Measurements" (2017). USF Tampa Graduate Theses and Dissertations.