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Radio channel state information (CSI) measured with many receivers is a goodresource for localizing a transmit device with machine learning with adiscriminative model. However, CSI localization is nontrivial when the radiomap is complicated, such as in building corridors. This paper introduces aview-selective deep learning (VSDL) system for indoor localization using CSI ofWiFi. The multiview training with CSI obtained from multiple groups of accesspoints (APs) generates latent features on supervised variational deep network.This information is then applied to an additional network for dominant viewclassification to enhance the regression accuracy of localization. Asnon-informative latent features from multiple views are rejected, we canachieve a localization accuracy of 0.77 m, which outperforms by 30 $\%$ thebest known accuracy in practical applications in a real building environment.To the best of our knowledge, this is the first approach to apply variationalinference and to construct a scalable system for radio localization.Furthermore, our work investigates a methodology for supervised learning withmultiview data where informative and non-informative views coexist.

Multiview Variational Deep Learning with Application to Scalable Indoor Localization