Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/36992
Title: | Towards Indoor Localization Guided by Machine Learning |
Authors: | ELAGGOUNE, Hocine ZERROUKI, Fatma Zahra OUARGLI, Lamia |
Keywords: | Indoor Geolocation Smartphone Wi-F Deep Learning LSTM (Long Short-Tem Memory) |
Issue Date: | 2024 |
Publisher: | UNIVERSITY OF KASDI MERBAH OUARGLA |
Citation: | FACULTY OF NEW TECHNOLOGIES OF INFORMATION AND COMMUNICATION |
Abstract: | Indoor geolocation has become a major focus in various fields, such as indoor navigation, warehouse logistics, and targeted marketing in shopping centers. Traditional geolocation technologies, like GPS, show their limitations in indoor environments due to signal loss caused by building structures. As a result, Wi-Fi networks and smartphone sensors present a promising alternative for indoor geolocation. This master's thesis aims to analyze existing work and propose a precise and robust indoor geolocation solution by integrating signals from surrounding Wi-Fi networks and smartphone sensors (such as accelerometers, gyroscopes, magnetometers, etc.). The primary objective is to design a data fusion algorithm that effectively combines information from these different sources to achieve accurate real-time localization. |
Description: | Telecommunication Systems |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/36992 |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
ZERROUKI-OUARGLI.pdf | Telecommunication Systems | 3,8 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.