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Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments.

Authors :
Liu, Junyang
Li, Yuan
Zhang, Yulu
Ma, Shuai
Li, Gui
He, Yi
Yi, Haiwen
Liu, Yue
Xu, Xiaotao
Zhang, Xu
He, Jinyao
Wen, Guangjun
Li, Jian
Source :
Computers, Materials & Continua; 2024, Vol. 80 Issue 1, p913-930, 18p
Publication Year :
2024

Abstract

Addressing the challenges of passive Radio Frequency Identification (RFID) indoor localization technology in Non-Line-of-Sight (NLoS) and multipath environments, this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival (SD-PDOA) and Received Signal Strength Indicator (RSSI). This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information, thereby facilitating high precision and stability in passive RFID localization. The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 reader. The findings are significant: in NLoS conditions, the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 m. In complex multipath environments, this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 m. When compared to conventional passive localization methods, our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath conditions. This research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things (IoT) system, marking a significant advancement in the field. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15462218
Volume :
80
Issue :
1
Database :
Complementary Index
Journal :
Computers, Materials & Continua
Publication Type :
Academic Journal
Accession number :
178740944
Full Text :
https://doi.org/10.32604/cmc.2024.049999