Wi-Fi Fingerprint-Based Indoor Mobile User Localization Using Deep Learning

In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localizat...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2021, Vol.2021 (1)
Main Authors: Bai, Junhang, Sun, Yongliang, Meng, Weixiao, Li, Cheng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Coders
Deep learning
Fingerprints
Global positioning systems
GPS
Internet of Things
Localization method
Location based services
Machine learning
Neural networks
Radio frequency identification
Recurrent neural networks
Signal strength
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Summary:In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.
ISSN:1530-8669
1530-8677
DOI:10.1155/2021/6660990