A UHF-RFID Gate Control System Based on a Recurrent Neural Network

This letter presents a novel, cost-effective, and easy-to-deploy solution to discriminate the direction of goods crossing a UHF radio frequency identification (RFID) gate in a warehouse scenario. The system is based on a grid of UHF-RFID tags deployed on the floor underneath the gate equipped with a single reader antenna. When a transpallet crosses the gate, it shadows the tags of the deployed grid differently, according to the specific direction, namely incoming or outgoing. Such distinguishable signature is employed as input of a recurrent neural network. In particular, the number of readings for each tag is aggregated within short time windows, and a sequence of binary read/missed tag data over the time is extracted. Such temporal sequences are used to train a long short-term memory neural network. Classification performance of the proposed method is shown through a set of measurements in an indoor scenario.