Some Signal Processing Techniques for Wireless Cooperative Localization and Tracking
type: Dataset
description: Advancements in information technology and communication systems enabled the development of a wide variety of location based applications and services. The global navigation satellite systems are among the fundamental localization solutions. In harsh environments (e.g., urban canyons and indoor areas), these solutions do not provide a good accuracy or even become unavailable. In order to offer accurate and ubiquitous localization solutions, wireless communication systems have been considered, where several location dependent parameters of the transmitted signals can be measured and exploited (e.g., the time-of-arrival (ToA), the received signal strength (RSS)). In this work, the topic of wireless localization is explored from a statistical signal processing perspective with a focus on two axes. The first axis is cooperative localization applied to ad-hoc networks, where the nodes perform pair-wise ranging measurements (i.e., ToA or RSS) between each other in order to simultaneously estimate their positions. The unique solvability conditions are studied based on the two approaches of graph rigidity and semidefinite programming, and the identifiability conditions are derived. Location estimation solutions are considered with a focus on probabilistic estimation and its application in Markov random fields using the nonparametric belief propagation (NBP) algorithm. The second axis is mobile terminals tracking based on RSS measurements. These measurements are affected by a shadowing phenomenon. The improvement brought by the knowledge of the shadowing maps to the position estimation accuracy is studied. The classical solution for obtaining these maps is fingerprinting, which can be costly in time and effort. Solutions are developed to overcome these difficulties. Several solutions are proposed and investigated via Monte Carlo simulations in different deployment and application scenarios, and several theoretical and practical results are derived.
issued: 2026-05-14T13:04:27.672178
language: en
modified: 2026-05-14T13:04:27.672184
contact point: Organization
  fn: CCSD
keyword: bayesian-filtering
keyword: champs-aleatoire-de-markov
keyword: cooperative-localization
keyword: filtrage-bayesien
keyword: filtrage-particulaire
keyword: fisher-information
keyword: geolocalisation-cooperative
keyword: graph-rigidity
keyword: identifiabilite
keyword: identifiability
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: information-de-fisher
keyword: markov-random-field
keyword: methodes-de-monte-carlo
keyword: monte-carlo-methods
keyword: nonparametric-belief-propagation
keyword: particle-filters
keyword: position-tracking
keyword: programmation-semi-definie
keyword: propagation-de-croyance-non-parametrique
keyword: rao-blackwellisation
keyword: rao-blackwellization
keyword: rigidite-graphique
keyword: semidefinite-programming
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: suivi-de-trajectoires
keyword: theses
landing page: https://theses.hal.science/tel-00787543

Some Signal Processing Techniques for Wireless Cooperative Localization and Tracking
type: Distribution
format: HTML
issued: 2026-05-14T13:04:27.757529
modified: 2026-05-14T13:04:27.623209
access URL: https://theses.hal.science/tel-00787543

test_moissonnage_selune
type: Agent