Hierarchical fingerprinting and feature extraction for indoor localization - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Hierarchical fingerprinting and feature extraction for indoor localization

Siyang Liu
  • Fonction : Auteur
  • PersonId : 183103
  • IdHAL : siyang-liu
Raul de Lacerda
Jocelyn Fiorina
  • Fonction : Auteur
  • PersonId : 1143316

Résumé

Weighted K nearest neighbor (WKNN) algorithm provides good result for indoor localization by searching for matching fingerprints in the dataset. However, due to the nature of this method, computation load increases as the size of dataset. In this paper, we propose a hierarchical localization method to reduce the computation load during the online phase. A twolevel structure first localizes the user to a smaller subset and then position estimation is obtained by WKNN with feature extraction. On a public accessed WiFi fingerprinting dataset, the proposed method achieves a 98% reduction without sacrificing localization performance.
Fichier principal
Vignette du fichier
Hierarchical fingerprinting and feature extraction for indoor localization.pdf (519.66 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03697734 , version 1 (17-06-2022)

Identifiants

Citer

Siyang Liu, Raul de Lacerda, Jocelyn Fiorina. Hierarchical fingerprinting and feature extraction for indoor localization. IEEE 2022 International Conference on Communications (ICC 2022), May 2022, Seoul, South Korea. ⟨10.1109/icc45855.2022.9839182⟩. ⟨hal-03697734⟩
40 Consultations
81 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More