Title :
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
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;
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
DOI :
10.1109/PIMRCW.2010.5670363
