Passive radio frequency identification sorting for dense objects on high-speed conveyor belts

Abstract

In many logistics applications, densely placed objects on high-speed conveyor belts are attached with passive tags for automatic identification and sorting to work more efficiently. Traditional localization or sorting methods cannot effectively work in such scenarios due to complex environments. In this article, we propose a new passive radio frequency identification sorting system for dense mobile tags on high-speed conveyor belts by utilizing the output phase. At first, a relative motion model is utilized to get the zero point time when the object passes the radio frequency identification portal. Then, the coarse-grained time and the corresponding speed of the conveyor belt are obtained by the phase curve fitting. Finally, the inverse synthetic aperture radio frequency identification is used to get the fine-grained zero point time, which is realized based on the holographic image reconstruction. And the particle filter is utilized to get a significant reduction of computational burden. The proposed method is implemented with commercial-off-the-shelf devices, and the evaluation results in various scenarios show our system can achieve an average accuracy of 97% with the tag density of 10/m and at a speed of 4 m/s.