Abstract: RFID is an established technology and its implementation has been increasing steadily in different industries in the last decades. An important and relatively recent RFID breakthrough has been that of moving the level of tagging from pallet- or case-level, to item-level. This development has opened up a new set of use cases and benefits, especially in retail. One of these new use cases is the estimation of items’ location by positioning and tracking the tags attached to them. This problem is often seen as a classification problem, especially when tags that are read at the retail store must be located either in the sales floor or in the backroom area. The typical approach to ease this classification consists of physically shielding the interested areas via hardware installations, although this solution is expensive and lacks flexibility. In this paper, we present a different solution, namely a software-based shielding approach, to address the classification problem. Our solution makes use of item-level RFID tags and is based on the well-known logistic regression. Whenever a reading session is performed by means of a handheld reader, the classification model estimates in real-time (i.e. within a few seconds) which tagged items are in the same area of the reader and which are not, with no need of any shielding hardware installation. According to the validation preliminary tests presented in this paper, in which we simulated a fashion retail store, the proposed approach has an overall average accuracy of 95.5%.
