An Indoor Localization Method Based on AOA and PDOA Using Virtual Stations in Multipath and NLOS Environments for Passive UHF RFID | Zendy

Yongtao Ma | Zendy; Bobo Wang | Zendy; Shuyang Pei | Zendy; Yunlei Zhang | Zendy; Shuai Zhang | Zendy; Jiexiao Yu | Zendy

Open Access

An Indoor Localization Method Based on AOA and PDOA Using Virtual Stations in Multipath and NLOS Environments for Passive UHF RFID

Author(s) -

Yongtao Ma,

Bobo Wang,

Shuyang Pei,

Yunlei Zhang,

Shuai Zhang,

Jiexiao Yu

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2838590

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Ultra-high frequency radio frequency identification (UHF RFID) localization technique has been considered increasingly promising in indoor positioning systems. However, conventional localization algorithms are vulnerable in multipath and non-line of sight (NLOS) environments. To solve this problem, this paper presents an indoor localization method based on angle of arrival and phase difference of arrival (PDOA) using virtual stations for passive UHF RFID. We use the array antenna to distinguish multipath signals and choose the two strongest paths according to the received signal strength to perform localization. The angles of the two paths are obtained through the phase difference of the received signals at different array elements, and the distances of the two paths are estimated through PDOA measurement. After obtaining the angles and distances, we establish virtual stations to convert NLOS paths into LOS paths. The possible positions of the tag are calculated through virtual stations, angle, and distance information, which are derived from the two signal paths. Then, the weighted least squares combined with residual weighted algorithm are proposed to calculate real position of the tag. Simulation results demonstrate that our method achieves decimeter level accuracy and has higher precision than traditional algorithms.

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