Estimation of RFID Tag Population Size by Gaussian Estimator

Radio Frequency IDentification (RFID) systems are prevalent in all sorts of daily life endeavors. Most previous tag estimation schemes worked with relatively smaller frame size and large number of rounds. Here we propose a new estimator named "Gaussian Estimator of RFID Tags,"(GERT), that works with large enough frame size to be accurately approximated to Gaussian distribution within a frame. The selection of the frame size is done according to Triangular Array Central Limit Theorem which also enables us to quantify the approximation error. Larger frame size helps the statistical average to converge faster to the ensemble mean of the estimator and the quantification of the approximation error helps to determine the number of rounds to keep up with the accuracy requirements. The overall performance of GERT is better than the previously proposed schemes considering the number of slots required for estimation to achieve a given level of estimation accuracy.