FaHo: deep learning enhanced holographic localization for RFID tags

In recent years, radio frequency identification (RFID)-based approaches have been demonstrated to be a promising indoor localization techniques for many valuable applications, such as tracking tagged objects on the manufacturing lines, locating items in smart warehouses, and so on. In the near future, many applications will gain great benefits from knowing the positions of RFID-tagged objects. However, existing localization approaches often suffer from severe accuracy degradation in real-world environments due to the prevalent environmental interferences, such as the multipath effects. To this end, we designed an RFID-based localization system FaHo, which leverages a deep learning enhanced holographic technique for locating RFID tags accurately even in complex indoor environments. By carefully analyzing the features of the traditional holographic method, we created a new hologram-based algorithm called joint hologram, which yields a robust likelihood for each assumed position to be the true tag position. FaHo then adopts a deep convolutional neural network for analyzing the whole hologram, and subsequently estimate the true location of the RFID tag rather than simply seek for the largest-likelihood location. Furthermore, we implemented FaHo and evaluated its performance in several multipath-rich scenarios. The experimental results show that FaHo can achieve centimeter-level accuracy in both the lateral and radial directions using only one moving antenna. More importantly, our work also demonstrates that hologram-based localization is a highly effective technique for RFID indoor localization tasks.