Paper Info
Title
Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers.
Abstract
A new approach to indoor localization is presented, based upon the use of Received Signal Strength (RSS) fingerprints containing data from very large numbers of cellular base stations--up to the entire GSM band of over 500 channels. Machine learning techniques are employed to extract good quality location information from these high-dimensionality input vectors. Experimental results in a domestic and an office setting are presented, in which data were accumulated over a 1-month period in order to assure time robustness. Room-level classification efficiencies approaching 100% were obtained, using Support Vector Machines in one-versus-one and one-versus-all configurations. Promising results using semi-supervised learning techniques, in which only a fraction of the training data is required to have a room label, are also presented. While indoor RSS localization using WiFi, as well as some rather mediocre results with low-carrier count GSM fingerprints, have been discussed elsewhere, this is to our knowledge the first study to demonstrate that good quality indoor localization information can be obtained, in diverse settings, by applying a machine learning strategy to RSS vectors that contain the entire GSM band.
Year
DOI
Venue
2011
10.1186/1687-1499-2011-81
EURASIP J. Wireless Comm. and Networking
Keywords
Field
DocType
Support Vector Machine, Receive Signal Strength, Indoor Localization, Standard Support Vector Machine, Base Transceiver Station
GSM,Computer science,Support vector machine,Communication channel,Computer network,Real-time computing,Robustness (computer science),Signal strength,RSS,Mobile telephony,Base transceiver station
Journal
Volume
Issue
ISSN
2011
1
1687-1499
Citations 
PageRank 
References 
27
0.59
16
Authors
4
Name
Order
Citations
PageRank
Y. Oussar129426.32
Iness Ahriz2373.89
B. Denby326826.69
Gérard Dreyfus447558.97