Open Access

ARTICLE

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

by Junyang Liu¹, Yuan Li², Yulu Zhang², Shuai Ma², Gui Li³, Yi He¹, Haiwen Yi¹, Yue Liu¹, Xiaotao Xu⁴, Xu Zhang¹, Jinyao He¹, Guangjun Wen¹, Jian Li^1,*

1 School of Information and Communication Engineering/Yibin Institute, University of Electronic Science and Technology of China, Chengdu, 611731, China
2 Department of IoT Technology Research, China Mobile Research Institute, Beijing, 100053, China
3 The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu, 611731, China
4 College of Information and Communication, National University of Defense Technology, Wuhan, 430019, China

* Corresponding Author: Jian Li. Email:

Computers, Materials & Continua 2024, 80(1), 913-930. https://doi.org/10.32604/cmc.2024.049999

Received 24 January 2024; Accepted 16 April 2024; Issue published 18 July 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.

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Keywords

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