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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Veröffentlicht in:International Conference on Indoor Positioning and Indoor Navigation S. 1 - 8
Hauptverfasser: Chidlovskii, Boris, Antsfeld, Leonid
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2019
Schlagworte:
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
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Zusammenfassung: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.
ISSN:2471-917X
DOI:10.1109/IPIN.2019.8911825