Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers
<p>Abstract</p> <p>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 ar...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2011-01-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Online Access: | http://jwcn.eurasipjournals.com/content/2011/1/81 |
| Summary: | <p>Abstract</p> <p>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 <it>one-versus-one </it>and <it>one-versus-all </it>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 <it>good quality </it>indoor localization information can be obtained, in diverse settings, by applying a machine learning strategy to RSS vectors <it>that contain the entire GSM band</it>.</p> |
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| ISSN: | 1687-1472 1687-1499 |