Supervised and semi-supervised deep probabilistic models for indoor positioning problems

•The WiFi fingerprint-based positioning problems are investigated.•A hybrid deep learning-based model is introduced.•A deep learning-based semi-supervised model is introduced.•The validation experiments are conducted to demonstrate the effectiveness of the proposed methods. WiFi fingerprint-based in...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 435; s. 228 - 238
Hlavní autoři: Qian, Weizhu, Lauri, Fabrice, Gechter, Franck
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 07.05.2021
Témata:
Indoor positioning
Mixture density networks
Semi-supervised learning
Variational autoencoders
WiFi fingerprints
Variational autoencoders
Mixture density networks
WiFi fingerprints
Semi-supervised learning
Indoor positioning
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí:•The WiFi fingerprint-based positioning problems are investigated.•A hybrid deep learning-based model is introduced.•A deep learning-based semi-supervised model is introduced.•The validation experiments are conducted to demonstrate the effectiveness of the proposed methods. WiFi fingerprint-based indoor localization has been a popular research topic recently. In this work, we propose two novel deep learning-based models, the convolutional mixture density recurrent neural network and the variational autoencoder-based semi-supervised learning model. The convolutional mixture density recurrent neural network is designed for indoor next location prediction, in which the advantages of convolutional neural networks, recurrent neural networks and mixture density networks are combined. Furthermore, since most of real-world WiFi fingerprint data are not labeled, we devise a variational autoencoder-based model to compute accurate user location in a semi-supervised learning manner. Finally, in order to evaluate the proposed models, we conduct the validation experiments on two real-world datasets. The final results are compared to other existing methods and verify the effectiveness of our approaches.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.12.131