Indoor Localization with an Autoencoder based Convolutional Neural Network

Nowadays, studies on indoor localization systems based on wireless systems are increasing widely. Indoor localization is the process of determining the location of objects or people inside a building. Global Navigation Satellite System (GPS) signals do not provide sufficient location data indoors be...

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Bibliographic Details
Published in:IEEE access Vol. 12; p. 1
Main Authors: Arslantas, Hatice, Okdem, Selcuk
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
autoencoder
Buildings
convolutional neural network
Convolutional neural networks
Deep learning
Encoding
Fingerprint recognition
Floors
Global navigation satellite system
Indoor environment
Indoor localization
Localization
Location awareness
Machine learning
Neural networks
Satellite navigation systems
Systems design
Training
Wi-Fi fingerprint localization
Wireless fidelity
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Nowadays, studies on indoor localization systems based on wireless systems are increasing widely. Indoor localization is the process of determining the location of objects or people inside a building. Global Navigation Satellite System (GPS) signals do not provide sufficient location data indoors because they are interrupted or completely lost in closed areas. For this reason, studies on indoor localization system design with machine learning and deep learning techniques based on Wi-Fi technology are increasing. In this study, we propose a method and training strategy that is entirely based on a Convolutional Neural Network (CNN) and a combined autoencoder that automatically extracts features from Wi-Fi fingerprint samples. In this model, we coupled an autoencoder and a CNN and we trained them simultaneously. Thus, we guarantee that the encoder and the CNN are trained simultaneously. The proposed system was evaluated on the UJIIndoorLoc and Tampere datasets. The experimental results show that the proposed model performs significantly better than the current state-of-the-art methods in terms of location coordinates (x, y) localization. In our study, runtime analysis is also presented to show the real-time performance of the network we proposed.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3382135