RFID Localization in Wireless Sensor Networks

Received signal strength (RSS)-based localization of people and assets through RFID has significant benefits for logistics, security and safety. However, the accuracy of RFID localization in wireless sensor networks suffers from unrealistic antenna gain pattern assumption, and the human body has a major effect on the gain pattern of the RFID badge that the person is wearing. In this book chapter, the gain pattern due to the effect of the human body is experimentally measured and modeled. A method is presented to estimate the model parameters from multiple RSS measurements. Two joint orientation and position estimators, four-dimensional (4D) maximum likelihood estimation (MLE) algorithm and alternating gain and position estimation (AGAPE) algorithm, are proposed to estimate the orientation and the position of the badge using RSS measurements from anchor nodes. A Bayesian lower bound on the mean squared error of the joint estimation is derived and comparedwith the Cramer-Rao boundwith an isotropic gain pattern. Both theoretical and experimental results show that the accuracy of position estimates can be improved with orientation estimates included in the localization system.

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