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Programmable Metasurface Antenna Assisted Indoor 2-D Positioning Using Dynamical Coding

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PRE
AI
J
Jiaxin Li
H
Huiming Yao
B
Bo Li
B
Baiying Taishi
Y
Yiyuan Hao
J
Jianchun Xu
K
Ke Bi
DOI:10.1109/LAWP.2025.3617445delete
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Abstract

Abstract

En 中文
Position estimation plays a pivotal role in indoor sensing and localization systems. However, conventional received signal strength (RSS)-based positioning methods exhibit significant limitations in both multipath interference suppression and environmental adaptability. A programmable metasurface antenna-assisted indoor 2-D positioning method integrating dynamic angle perception and a path loss model modification is proposed. This method establishes a polar coordinate system centered on the metasurface antenna, enabling simultaneous determination of the angular position and radial distance of the human in the horizontal plane. The angular detection range is from +30° to –30° with a resolution of 15°, while the radial detection range is from 0.4 m to 1.2 m. Our scheme adopts rapid metasurface beam scanning and environmental difference calculation to achieve robust angle estimation. Simultaneously, distance measurement accuracy is substantially improved by incorporating distance-dependent polynomial correction terms into the path loss model. The cumulative density function shows 80% distance estimation error for indoor environment is lower than 0.13 m in experimental validation. Compared with the traditional RSS positioning method, the proposed method has significant advantages in multipath interference suppression, environmental adaptability, response speed, algorithm complexity and deployment convenience. It has important application value in medical monitoring and robot positioning.
Keywords:
Indoor positioning
metasurface
path loss
received signal strength

Journal

IEEE Antennas and Wireless Propagation Letters cover
IEEE Antennas and Wireless Propagation Letters
IF:
4.8
Papers:
1.0W
Citations:
2.8W

Organization

B
Beijing University of Posts and Telecommunications
Scholars:
2.6K
Papers: 1.2K
Citations: 4.2K