Multiview Variational Deep Learning With Application to Practical Indoor Localization

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 47
  • Download : 0
Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmitter device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This article introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on a supervised variational deep network. This information is then applied to an additional network for dominant view classification to minimize the regression loss of localization. As noninformative latent features from multiple views are rejected, we can achieve a localization accuracy of 1.28 m, which outperforms by 30% the best known accuracy in practical applications in a complex building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a practical system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and noninformative views coexist.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-08
Language
English
Article Type
Article
Citation

IEEE INTERNET OF THINGS JOURNAL, v.8, no.15, pp.12375 - 12383

ISSN
2327-4662
DOI
10.1109/jiot.2021.3063512
URI
http://hdl.handle.net/10203/287245
Appears in Collection
CS-Journal Papers(저널논문)EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0