MulTLoc: RF Hologram Tensor Filtering and Upscaling for Locating Multiple RFID Tags

In this paper, we present MulTLoc, a deep learning based indoor localization system for localizing multiple ultra-high frequency (UHF) passive RFID tags with RF hologram tensor filtering and upscaling. The proposed system leverages the RF hologram tensor as the input of the deep convolutional networks. The RF hologram tensor exhibits a strong relationship between the observation and the spatial location, which enhances the robustness of the system to the dynamic environment and equipment. To sanitize the RF hologram tensor, two architectures of deep networks are newly proposed. The hologram filter network suppresses the fake peaks resulting from the multipath and phase wrapping by leveraging the spatial relationship between tags. The tensor upscaling network recovers the high resolution hologram tensor from the output of the previous network, which enhances the localization accuracy of the system further. Comparing with the fingerprinting based localization systems using deep networks as the classifier, the networks in the MulTLoc system treat the localization problem as the regression problem, in which the ambiguity between fingerprints is reserved. To avoid the inherent errors in the fingerprinting based localization systems, the location estimation is given by intuitive peak finding algorithms using the recovered RF hologram tensor. We implement the proposed MulTLoc system with commodity RFID devices and verify its performance with extensive experiments.