RFID reader localization using hard decisions with error concealment

This paper presents an efficient reader localization algorithm in radio frequency identification (RFID) networks. In the proposed algorithm, it is assumed that no channel state information is available at the reader and the backscatters of all RFID tags are converted to hard decisions using an energy detector. The accuracy of the estimated location is improved using an error concealment algorithm that utilizes the fact that adjacent tags are expected to produce similar decisions. The final location estimates are obtained using particle swarm optimization of a maximum likelihood estimator that takes into account imperfections in the sensing and transmission processes. The performance of the proposed algorithm is evaluated in terms of the root mean square error using Monte Carlo simulation, and compared to other well-established localization algorithms. Moreover, Cramer-Rao lower bound is derived to assess the efficiency of the new estimator. The obtained results show that the proposed algorithm estimation accuracy is up to 26.5% more than the other benchmark estimators.