Open AccessIndoor Localization with an Autoencoder based Convolutional Neural Network
Author(s) -
Hatice Arslantas,
Selcuk Okdem
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3382135
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.