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Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach
Joint Authors
Dreyfus, Gérard
Ahriz, Iness
Oussar, Yacine
Denby, Bruce
Source
International Journal of Navigation and Observation
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-06-24
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Indoor handset localization in an urban apartment setting is studied using GSM trace mobile measurements.
Nearest-neighbor, Support Vector Machine, Multilayer Perceptron, and Gaussian Process classifiers are compared.
The linear Support Vector Machine provides mean room classification accuracy of almost 98% when all GSM carriers are used.
To our knowledge, ours is the first study to use fingerprints containing all GSM carriers, as well as the first to suggest that GSM can be useful for localization of very high performance.
American Psychological Association (APA)
Ahriz, Iness& Oussar, Yacine& Denby, Bruce& Dreyfus, Gérard. 2010. Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach. International Journal of Navigation and Observation،Vol. 2010, no. 2010, pp.1-7.
https://search.emarefa.net/detail/BIM-476430
Modern Language Association (MLA)
Ahriz, Iness…[et al.]. Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach. International Journal of Navigation and Observation No. 2010 (2010), pp.1-7.
https://search.emarefa.net/detail/BIM-476430
American Medical Association (AMA)
Ahriz, Iness& Oussar, Yacine& Denby, Bruce& Dreyfus, Gérard. Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach. International Journal of Navigation and Observation. 2010. Vol. 2010, no. 2010, pp.1-7.
https://search.emarefa.net/detail/BIM-476430
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-476430