The ARPEGEO project: A new look at cellular RSSI fingerprints for localization

Author

Ahriz, Iness ; Denby, Bruce ; Dreyfus, Gérard ; Dubois, Rémi ; Roussel, Pierre

Author_Institution

SIGMA (Signal Process. & Machine learning) Lab., ESPCI ParisTech, Paris, France

fYear

2010

Firstpage

208

Lastpage

212

Abstract

A new technique developed at ESPCI ParisTech should allow cellular received signal strength fingerprints to play an important role in localization systems for regions that are not well covered by GPS. The article describes the ARPEGEO project, initiated to evaluate the impact of full-band GSM fingerprints analyzed with modern machine learning techniques. Results on indoor localization, as well as techniques to facilitate practical implementation of the method, are presented.

Keywords

cellular radio; learning (artificial intelligence); mobility management (mobile radio); telecommunication computing; ARPEGEO project; cellular RSSI fingerprints; full-band GSM fingerprints; indoor localization; machine learning; received signal strength indicator; Classification algorithms; Conferences; Fingerprint recognition; GSM; Mobile communication; Support vector machines; Training; GSM; fingerprint; indoor; localization; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Personal, Indoor and Mobile Radio Communications Workshops (PIMRC Workshops), 2010 IEEE 21st International Symposium on

Conference_Location

Instanbul

Print_ISBN

978-1-4244-9117-9

Type

conf

DOI

10.1109/PIMRCW.2010.5670363

Filename

5670363