Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloIndoor positioning based on fingerprint-image and deep learning
Anno di pubblicazione2018
FormatoElettronico
Autore/iShao W.; Luo H.; Zhao F.; Ma Y.; Zhao Z.; Crivello A.
Affiliazioni autoriSchool of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China; Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China; Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China; Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China; Institute of Computer Science, University of Bern, Bern, Switzerland; CNR-ISTI, Pisa, Italy
Autori CNR e affiliazioni
  • ANTONINO CRIVELLO
Lingua/e
  • inglese
AbstractWi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the stateof-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da74699
Pagine a74712
Pagine totali-
RivistaIEEE access
Attiva dal 2013
Editore: Institute of Electrical and Electronics Engineers - Piscataway, NJ
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2169-3536
Titolo chiave: IEEE access
Titolo proprio: IEEE access
Numero volume della rivista6
Fascicolo della rivista1
DOI10.1109/ACCESS.2018.2884193
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85057790544)
  • ISI Web of Science (WOS) (Codice:WOS:000454390300001)
Parole chiaveIndoor Positioning, Indoor Localization, Neural networks, Fingerprint, Feature extraction
Link (URL, URI)https://ieeexplore.ieee.org/document/8554268
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione27/11/2018
Note/Altre informazioni-
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Indoor Positioning Based on Fingerprint-Image and Deep Learning
Descrizione: OA Journal
Tipo documento: application/pdf