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Supervised and semi-supervised deep probabilistic models for indoor positioning problems
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DOI：10.1016/j.neucom.2020.12.131.png)
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Abstract
En 中文
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 net-work 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 com-bined. Furthermore, since most of real-world WiFi fingerprint data are not labeled, we devise a varia-tional 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 effec-tiveness of our approaches. CO 2021 Elsevier B.V. All rights reserved.
Keywords:
Variational autoencoders
Mixture density networks
Semi-supervised learning
WiFi fingerprints
Indoor positioning
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