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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Vydané v:	IEEE access Ročník 12; s. 1
Hlavní autori:	Arslantas, Hatice, Okdem, Selcuk
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Arslantas, Hatice Okdem, Selcuk
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SubjectTerms	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
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Title	Indoor Localization with an Autoencoder based Convolutional Neural Network
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