Enhancing RFID Observations Using Masked Autoencoders for Improved Indoor Localization

This paper presents the MAE model that uses a Masked AutoEncoder (MAE) to enhance the observations from commercial passive Radio-Frequency Identification (RFID) devices. It is crucial to address the common issue of RFID readers failing to collect observations from all their hop channels and antennas due to environmental effects and device limitations. The proposed method examines the inner rationale among observations from various channels and antennas to reconstruct the missing observations, which can significantly improve the performance of downstream applications. The experiment results show that when we collect more than 70% observation in all antennas at all channels, our MAE model can restore 90% of the missing phase with an error of less than 0.1 radians, which is even less than the error caused by thermal noise in an RFID system. Our MAE model's accuracy in restoring missing data provides a promising future to improve the performance of various RFID applications like localization and motion tracking by providing more complete observations.