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OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning

In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing w...

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Autores principales: Gidey, Hailu Tesfay, Guo, Xiansheng, Zhong, Ke, Li, Lin, Zhang, Yukun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735931/
https://www.ncbi.nlm.nih.gov/pubmed/36501747
http://dx.doi.org/10.3390/s22239044
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author Gidey, Hailu Tesfay
Guo, Xiansheng
Zhong, Ke
Li, Lin
Zhang, Yukun
author_facet Gidey, Hailu Tesfay
Guo, Xiansheng
Zhong, Ke
Li, Lin
Zhang, Yukun
author_sort Gidey, Hailu Tesfay
collection PubMed
description In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces ([Formula: see text]) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model’s overfitting problem.
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spelling pubmed-97359312022-12-11 OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning Gidey, Hailu Tesfay Guo, Xiansheng Zhong, Ke Li, Lin Zhang, Yukun Sensors (Basel) Article In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces ([Formula: see text]) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model’s overfitting problem. MDPI 2022-11-22 /pmc/articles/PMC9735931/ /pubmed/36501747 http://dx.doi.org/10.3390/s22239044 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gidey, Hailu Tesfay
Guo, Xiansheng
Zhong, Ke
Li, Lin
Zhang, Yukun
OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning
title OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning
title_full OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning
title_fullStr OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning
title_full_unstemmed OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning
title_short OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning
title_sort ohettlal: an online transfer learning method for fingerprint-based indoor positioning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735931/
https://www.ncbi.nlm.nih.gov/pubmed/36501747
http://dx.doi.org/10.3390/s22239044
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