Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and costefficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5Gbased positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.
%0 Generic
%1 vietz2022learningbased
%A Vietz, Hannes
%A Löcklin, Andreas
%A Ben Haj Ammar, Hamza
%A Weyrich, Michael
%B 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022
%D 2022
%K 2022ias ias
%T Deep learning-based 5G indoor positioning in a manufacturing environment
%X Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and costefficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5Gbased positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.
@conference{vietz2022learningbased,
abstract = {Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and costefficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5Gbased positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.},
added-at = {2022-10-17T13:10:38.000+0200},
author = {Vietz, Hannes and Löcklin, Andreas and Ben Haj Ammar, Hamza and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c5961fe292f3084b30b6b2e5310d8c4d/taylansngerli},
booktitle = {2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022 },
interhash = {9548f6dc327234f37abc473704d5495b},
intrahash = {c5961fe292f3084b30b6b2e5310d8c4d},
keywords = {2022ias ias},
timestamp = {2022-10-31T10:10:57.000+0100},
title = {Deep learning-based 5G indoor positioning in a manufacturing environment},
year = 2022
}
