Maximum Likelihood Approach for RFID Tag Cardinality Estimation under Capture Effect and Detection Errors

Cardinality estimation schemes of Radio Frequency IDentification (RFID) tags using Framed Slotted ALOHA (FSA) based protocol are studied in this paper. Not as same as previous estimation schemes, we consider tag cardinality estimation problem under not only detection errors but also capture effect, where a tag’s IDentity (ID) might not be detected even in a singleton slot, while it might be identified even in a collision slot due to the fading of wireless channels. Maximum Likelihood (ML) approach is utilized for the estimation of the detection error probability, the capture effect probability, and the tag cardinality. The performance of the proposed method is evaluated under different system parameters via computer simulations to show the method’s effectiveness comparing to other conventional approaches. key words: RFID, Maximum Likelihood (ML), tag cardinality estimation, capture effect, detection errors