Semi-supervised Variational Autoencoder for WiFi Indoor Localization

We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencode...

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Published in:International Conference on Indoor Positioning and Indoor Navigation pp. 1 - 8
Main Authors: Chidlovskii, Boris, Antsfeld, Leonid
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2019
Subjects:
Buildings
Data collection
Deep learning
Predictive models
semi-supervised learning
Semisupervised learning
Task analysis
UJI-IndoorLoc dataset
variational auto-encoder
WiFi based indoor localization
Wireless fidelity
ISSN:2471-917X
Online Access:Get full text
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Abstract We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencoder deep network. We complement the network with an additional component of structural projection able to further improve the localization accuracy in a complex, multi-building and multi-floor environment. We consider several different network compositions which combine the classification and regression sub-tasks to achieve optimal performance. We evaluate our method on the public UJI-IndoorLoc dataset and show that the proposed method allows to maintain the state of the art localization accuracy with a very limited amount of annotated data.
AbstractList We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencoder deep network. We complement the network with an additional component of structural projection able to further improve the localization accuracy in a complex, multi-building and multi-floor environment. We consider several different network compositions which combine the classification and regression sub-tasks to achieve optimal performance. We evaluate our method on the public UJI-IndoorLoc dataset and show that the proposed method allows to maintain the state of the art localization accuracy with a very limited amount of annotated data.
Author Antsfeld, Leonid
Chidlovskii, Boris
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  givenname: Leonid
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  fullname: Antsfeld, Leonid
  organization: Naver Labs Europe,Meylan,France,38240
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Snippet We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction...
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StartPage 1
SubjectTerms Buildings
Data collection
Deep learning
Predictive models
semi-supervised learning
Semisupervised learning
Task analysis
UJI-IndoorLoc dataset
variational auto-encoder
WiFi based indoor localization
Wireless fidelity
Title Semi-supervised Variational Autoencoder for WiFi Indoor Localization
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