RFID Location Algorithm Based on Target Search and Repeat Calibration | Zendy

Guangshun Li | Zendy; Chenglong Li | Zendy; Junhua Wu | Zendy; Yanmin Yin | Zendy

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RFID Location Algorithm Based on Target Search and Repeat Calibration

Author(s) -

Guangshun Li,

Chenglong Li,

Junhua Wu,

Yanmin Yin

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.01.271

Subject(s) - computer science , euclidean distance , point (geometry) , algorithm , calibration , range (aeronautics) , search algorithm , positioning technology , positioning system , basis (linear algebra) , point location , precise point positioning , computer vision , artificial intelligence , real time computing , global positioning system , mathematics , telecommunications , statistics , materials science , geometry , gnss applications , composite material

In the RFID indoor positioning system, how to realize the positional positioning of the tag to be positioned is an important issue on the basis of ensuring the positioning accuracy. In order to solve this problem, a new search method is proposed in this paper. Firstly, the positioning area to which the tag to be located belongs is determined, the positioning range is narrowed, and the center point of the positioning area is selected as the search starting point, and the target search is performed in a certain step along the six directions in the positioning area until the virtual point RSSI value is searched. When the Euclidean distance between the signal strength value of the tag to be located satisfies the accuracy e , the point coordinate is output, and then improve the positioning accuracy by introducing repeat calibration technology. Finally, the influence of search distance on the improved algorithm is observed by adjusting the size of search step. The simulation results show that the minimum positioning error of the new algorithm is 0.5m and the average positioning accuracy is improved by 40% compared with the original algorithm.

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