Scalable RFID Tag Estimator With Enhanced Accuracy and Low Estimation Time

The knowledge of the number of tags is critical in many radio frequency identification (RFID) applications. This paper is concerned with the problem of estimating an RFID tag population size when the number of tags is much higher than the frame size. A novel estimation scheme called "Scalable Minimum Mean Square Error" (sMMSE) is proposed. The proposed estimator updates the frame size by considering two key parameters: One determines the limit of the slots occupancy at which the frame size should be increased, and the other one sets the frame size increase factor. A formal study is provided to adjust these parameters with the aim of lowering the estimation error while scaling to highly populated tag sets. Numerical results indicate that sMMSE significantly decreases the normalized estimation error and maintains a low estimation time compared to existing strategies.