A framework for locating multiple RFID tags using RF hologram tensors
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 spa...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823001803 |
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| author | Xiangyu Wang Jian Zhang Shiwen Mao Senthilkumar CG Periaswamy Justin Patton |
| author_facet | Xiangyu Wang Jian Zhang Shiwen Mao Senthilkumar CG Periaswamy Justin Patton |
| author_sort | Xiangyu Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-243e204bc6f74b98b6de97d1bda1bdfc |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-243e204bc6f74b98b6de97d1bda1bdfc2025-08-20T03:49:03ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482025-04-0111233734810.1016/j.dcan.2023.12.004A framework for locating multiple RFID tags using RF hologram tensorsXiangyu Wang0Jian Zhang1Shiwen Mao2Senthilkumar CG Periaswamy3Justin Patton4Dept. of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849-5201, USA; RFID Lab, Auburn University, Auburn, AL 36849, USADepartment of Electrical and Computer Engineering, Kennesaw State University, Kennesaw, GA 30144, USADept. of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849-5201, USA; Corresponding author.RFID Lab, Auburn University, Auburn, AL 36849, USARFID Lab, Auburn University, Auburn, AL 36849, USAIn 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.http://www.sciencedirect.com/science/article/pii/S2352864823001803Radio-frequency identification (RFID)Ultra-high frequency (UHF) passive RFID tagRF hologram tensorIndoor localizationDeep learning (DL)Swin Transformer |
| spellingShingle | Xiangyu Wang Jian Zhang Shiwen Mao Senthilkumar CG Periaswamy Justin Patton A framework for locating multiple RFID tags using RF hologram tensors Digital Communications and Networks Radio-frequency identification (RFID) Ultra-high frequency (UHF) passive RFID tag RF hologram tensor Indoor localization Deep learning (DL) Swin Transformer |
| title | A framework for locating multiple RFID tags using RF hologram tensors |
| title_full | A framework for locating multiple RFID tags using RF hologram tensors |
| title_fullStr | A framework for locating multiple RFID tags using RF hologram tensors |
| title_full_unstemmed | A framework for locating multiple RFID tags using RF hologram tensors |
| title_short | A framework for locating multiple RFID tags using RF hologram tensors |
| title_sort | framework for locating multiple rfid tags using rf hologram tensors |
| topic | Radio-frequency identification (RFID) Ultra-high frequency (UHF) passive RFID tag RF hologram tensor Indoor localization Deep learning (DL) Swin Transformer |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823001803 |
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