Maximum Likelihood Approach for RFID Tag Set Cardinality Estimation with Detection Errors

Chuyen T. Nguyen, Kazunori Hayashi, Megumi Kaneko, Petar Popovski, Hideaki Sakai

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)
763 Downloads (Pure)

Abstract

Abstract Estimation schemes of Radio Frequency IDentification (RFID) tag set cardinality are studied in this paper using Maximum Likelihood (ML) approach. We consider the estimation problem under the model of multiple independent reader sessions with detection errors due to unreliable radio communication links and/or collisions. In every reader session, both the detection error probability and the total number of tags are estimated. In particular, after the R-th reader session, the number of tags detected in j ( j = 1;2; :::;R) reader sessions out of R sessions is updated, which we call observed evidence. Then, in order to maximize the likelihood function of the number of tags and the detection error probability given the observed evidences, we propose three dierent estimation methods depending on how to treat the discrete nature of the tag set cardinality. The performance of the proposed methods is evaluated
under dierent system parameters and compared with that of the conventional
method via computer simulations assuming flat Rayleigh fading environments and
framed-slotted ALOHA based protocol.
Keywords RFID tag cardinality estimation maximum likelihood detection error
Original languageEnglish
JournalWireless Personal Communications
Volume71
Issue number4
Pages (from-to)2587-2603
Number of pages17
ISSN0929-6212
DOIs
Publication statusPublished - 2013

Cite this