Paper Info
Title
Semi-supervised Variational Autoencoder for WiFi Indoor Localization
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 autoencoder 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.
Year
DOI
Venue
2019
10.1109/IPIN.2019.8911825
2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Keywords
Field
DocType
WiFi based indoor localization,semi-supervised learning,variational auto-encoder,UJI-IndoorLoc dataset
Computer vision,Autoencoder,Artificial intelligence,Engineering
Conference
ISSN
ISBN
Citations 
2162-7347
978-1-7281-1789-8
2
PageRank 
References 
Authors
0.38
10
2
Name
Order
Citations
PageRank
Boris Chidlovskii141152.58
Leonid Antsfeld220.72