| Issue |
Sci. Tech. Energ. Transition
Volume 80, 2025
|
|
|---|---|---|
| Article Number | 48 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.2516/stet/2025028 | |
| Published online | 21 August 2025 | |
Regular Article
MULTI-phase: A passive RFID positioning system based on multi-frequency phase difference
1
School of Information Engineering (School of Big Data), Xuzhou University of Technology, Xuzhou 221000, PR China
2
School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, PR China
* Corresponding author: zhyh@xzit.edu.cn
Received:
16
May
2025
Accepted:
22
July
2025
Accurate and rapid indoor positioning is essential for realizing the Internet of Things. Although satellite-based positioning works well outdoors, it often fails indoors due to signal blockage. Radio Frequency Identification (RFID) positioning technology has attracted attention due to its low cost, ease of deployment, and contactless operation. However, traditional methods based on Received Signal Strength Indicator (RSSI) are vulnerable to environmental interference, reducing accuracy. This paper proposes a passive RFID indoor positioning method that uses multi-frequency phase differences ranging. A Gauss-Kalman filter is applied to reduce the influence of environmental noise on phase data. A multi-frequency phase difference distance model is established, and the Chinese Remainder Theorem with a closed-form solution is used to solve phase ambiguity. Finally, a weighted Levenberg-Marquardt algorithm refines the target position, improving convergence and accuracy. Experiments conducted in different indoor environments demonstrate that the proposed method achieves an average positioning error of 0.38 m and a maximum error of 0.5 m, meeting the requirements of indoor positioning applications.
Key words: Phase error / Phase difference ranging / Chinese remainder theorem / Weighted LM
© The Author(s), published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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