Cargando…

SmartFPS: Neural network based wireless-inertial fusion positioning system

Current wireless-inertial fusion positioning systems widely adopt empirical propagation models of wireless signals and filtering algorithms such as the Kalman filter or the particle filter. However, empirical models of system and noise usually have lower accuracy in a practical positioning scenario....

Descripción completa

Detalles Bibliográficos
Autores principales: Hua, Luchi, Zhuang, Yuan, Yang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950267/
https://www.ncbi.nlm.nih.gov/pubmed/36845067
http://dx.doi.org/10.3389/fnbot.2023.1121623
_version_ 1784893128977678336
author Hua, Luchi
Zhuang, Yuan
Yang, Jun
author_facet Hua, Luchi
Zhuang, Yuan
Yang, Jun
author_sort Hua, Luchi
collection PubMed
description Current wireless-inertial fusion positioning systems widely adopt empirical propagation models of wireless signals and filtering algorithms such as the Kalman filter or the particle filter. However, empirical models of system and noise usually have lower accuracy in a practical positioning scenario. The biases of predetermined parameters would enlarge the positioning error through layers of systems. Instead of dealing with empirical models, this paper proposes a fusion positioning system based on an end-to-end neural network, along with a transfer learning strategy for improving the performance of neural network models for samples with different distributions. Verified by Bluetooth-inertial positioning in a whole floor scenario, the mean positioning error of the fusion network was 0.506 m. The proposed transfer learning method improved the accuracy of the step length and rotation angle of different pedestrians by 53.3%, the Bluetooth positioning accuracy of various devices by 33.4%, and the average positioning error of the fusion system by 31.6%. The results showed that our proposed methods outperformed filter-based methods in challenging indoor environments.
format Online
Article
Text
id pubmed-9950267
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99502672023-02-25 SmartFPS: Neural network based wireless-inertial fusion positioning system Hua, Luchi Zhuang, Yuan Yang, Jun Front Neurorobot Neuroscience Current wireless-inertial fusion positioning systems widely adopt empirical propagation models of wireless signals and filtering algorithms such as the Kalman filter or the particle filter. However, empirical models of system and noise usually have lower accuracy in a practical positioning scenario. The biases of predetermined parameters would enlarge the positioning error through layers of systems. Instead of dealing with empirical models, this paper proposes a fusion positioning system based on an end-to-end neural network, along with a transfer learning strategy for improving the performance of neural network models for samples with different distributions. Verified by Bluetooth-inertial positioning in a whole floor scenario, the mean positioning error of the fusion network was 0.506 m. The proposed transfer learning method improved the accuracy of the step length and rotation angle of different pedestrians by 53.3%, the Bluetooth positioning accuracy of various devices by 33.4%, and the average positioning error of the fusion system by 31.6%. The results showed that our proposed methods outperformed filter-based methods in challenging indoor environments. Frontiers Media S.A. 2023-02-10 /pmc/articles/PMC9950267/ /pubmed/36845067 http://dx.doi.org/10.3389/fnbot.2023.1121623 Text en Copyright © 2023 Hua, Zhuang and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Hua, Luchi
Zhuang, Yuan
Yang, Jun
SmartFPS: Neural network based wireless-inertial fusion positioning system
title SmartFPS: Neural network based wireless-inertial fusion positioning system
title_full SmartFPS: Neural network based wireless-inertial fusion positioning system
title_fullStr SmartFPS: Neural network based wireless-inertial fusion positioning system
title_full_unstemmed SmartFPS: Neural network based wireless-inertial fusion positioning system
title_short SmartFPS: Neural network based wireless-inertial fusion positioning system
title_sort smartfps: neural network based wireless-inertial fusion positioning system
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950267/
https://www.ncbi.nlm.nih.gov/pubmed/36845067
http://dx.doi.org/10.3389/fnbot.2023.1121623
work_keys_str_mv AT hualuchi smartfpsneuralnetworkbasedwirelessinertialfusionpositioningsystem
AT zhuangyuan smartfpsneuralnetworkbasedwirelessinertialfusionpositioningsystem
AT yangjun smartfpsneuralnetworkbasedwirelessinertialfusionpositioningsystem