Fusing different measurements and algorithms to improve RFID localization accuracy

Abstract

This paper considers the topic of two-dimensional object localization using radio frequency identification (RFID) technology. One of the important problems that arise during the development of RFID positioning systems is choosing a localization algorithm and a type of measurement data. Usually developers try to find such a combination of an algorithm and measurement type that allows to achieve maximal localization accuracy for a particular scenario. However, in some situations there can be several combinations of algorithms and measurements with equally high accuracy. In order to simplify the choosing problem and to additionally increase accuracy it is developed a combinational localization method. The method is based on averaging position estimates formed by several point-based and zone-based algorithms that process different measurements. In our work there are used three point-based and three zone-based algorithms: a k nearest neighbors algorithm, trilateration, intersectional algorithm, the methods of support vector machine, artificial neural networks, and a naive Bayes classifier. As an input for the algorithms we utilized received signal strength, read rate, and proximity measurements. During the experiments we found that our method decreases the mean error by 15% and the maximum error by 14% compared to the best single algorithm.