Research Area:  Machine Learning

Abstract:

WiFi fingerprint-based indoor localization has been a popular research topic recently. In this work, we propose two novel deep learning-based models, the convolutional mixture density recurrent neural network and the variational autoencoder-based semi-supervised learning model. The convolutional mixture density recurrent neural network is designed for indoor next location prediction, in which the advantages of convolutional neural networks, recurrent neural networks and mixture density networks are combined. Furthermore, since most of real-world WiFi fingerprint data are not labeled, we devise a variational autoencoder-based model to compute accurate user location in a semi-supervised learning manner. Finally, in order to evaluate the proposed models, we conduct the validation experiments on two real-world datasets. The final results are compared to other existing methods and verify the effectiveness of our approaches.

Keywords:  
Supervised
Semi-supervised
Deep probabilistic models
WiFi fingerprint data
Semi-supervised learning

Author(s) Name:  Weizhu Qian, Fabrice Lauri, Franck Gechter

Publisher name:  Elsevier

DOI:  10.1016/j.neucom.2020.12.131

Volume Information:  Volume 435

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0925231221000229