A framework for locating multiple RFID tags using RF hologram tensors^☆,☆☆

Abstract

In this paper, we present a Deep Neural Network (DNN) based framework that employs Radio Frequency (RF) hologram tensors to locate multiple Ultra-High Frequency (UHF) passive Radio-Frequency Identification (RFID) tags. The RF hologram tensor exhibits a strong relationship between observation and spatial location, helping to improve the robustness to dynamic environments and equipment. Since RFID data is often marred by noise, we implement two types of deep neural network architectures to clean up the RF hologram tensor. Leveraging the spatial relationship between tags, the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping. In contrast to fingerprinting-based localization systems that use deep networks as classifiers, our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints. We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors. The proposed framework is implemented using commodity RFID devices, and its superior performance is validated through extensive experiments.

Keywords

Radio-frequency identification (RFID) / Ultra-high frequency (UHF) passive RFID tag / RF hologram tensor / Indoor localization / Deep learning (DL) / Swin Transformer / Self-supervised learning

CRediT authorship contribution statement

Xiangyu Wang: Data curation, Investigation, Methodology, Validation, Writing - original draft. Jian Zhang: Conceptualization. Shiwen Mao: Conceptualization, Methodology, Supervision, Writing - review & editing. Senthilkumar CG Periaswamy: Project administration. Justin Patton: Project administration.

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