Indoor geo-location static and dynamic geo-location of mobile terminals in indoor environments
type: Dataset
description: Recently, the static and dynamic geo-location of a device or a person has become one of the most important aspects of communication systems because of its multiple applications. In general, knowing the position of a mobile terminal (MT) in outdoor or indoor environments is of major importance for applications providing services based on the location. The development of localization systems has been mainly driven by the avail- ability of the affordable cost of indoor wireless local area network (WLAN) infrastructure. There exist different techniques to localize MTs with the different mainly depending on the type of the metrics extracted from the radio frequency signals communicated between base stations (BSs) and MTs. Ideal measurements are taken in environments which are free of obstacles and in direct ray tracings between BS and MT. This is not the case in indoor environment because the daily use of permanent obstacles in the work space scatters the ray tracings. Measurements taken in Non Line Of Sight (NLOS) are unpredictable and different from those taken in LOS. In order to reduce measurement errors, one can apply different techniques such as mitigation, approximation, prior correction, or filtering. Tracking systems (TSs) have many concrete applications in the space of individual navigation, social net- working, asset management, traffic management, mobile resource management, etc. Different techniques are applied to build TSs in indoor environments, where the signal is noisy, weak or even non-existent. While the Global Positioning System (GPS) devices work well outside buildings and in urban canyons, tracking an indoor user in a real-world environment is much more problematic. The prediction problem remains an essential obstacle to construct reliable indoor TSs. Then lacks of reliable wireless signals represent the main issue for indoor geo-location systems. This obviously calls for some sort of predictions and corrections to overcome signal reliability, which…
identifier: NNT: 2012PEST1070
issued: 2026-05-14T03:27:32.534928
language: en
modified: 2026-05-14T03:27:32.534942
contact point: Organization
  fn: CCSD
keyword: fractional-integration
keyword: indoor-localisation
keyword: indoor-localization
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: integration-fractionnaire
keyword: nearest-neighbor
keyword: neural-networks
keyword: plus-proches-voisins
keyword: reseaux-de-neurones
keyword: reseaux-sans-fils
keyword: theses
keyword: tracking
keyword: wireless-networks
landing page: https://theses.hal.science/tel-00794586

Indoor geo-location static and dynamic geo-location of mobile terminals in indoor environments
type: Distribution
format: HTML
issued: 2026-05-14T03:27:32.647605
modified: 2026-05-14T03:27:32.480588
access URL: https://theses.hal.science/tel-00794586

test_moissonnage_selune
type: Agent