Issue No. 09 - September (2009 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2009.37
Inmaculada Mora-Jiménez , University Rey Juan Carlos, Fuenlabrada
Mark Wilby , University Rey Juan Carlos, Madrid
Estrella Everss , University Rey Juan Carlos, Madrid
Carlos Figuera , University Rey Juan Carlos, Fuenlabrada
Javier Ramos-López , University Rey Juan Carlos, Madrid
Alicia Guerrero-Curieses , University Rey Juan Carlos, Madrid
José Luis Rojo-Álvarez , University Rey Juan Carlos, Madrid
Indoor Location (IL) using Received Signal Strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (WiFi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our WiFi network, showing relevant information relating to the different fingerprinting schemes that can be used.
Received signal strength, indoor location, fingerprinting, uncertainty, leave one out, bootstrap resampling, IEEE 802.11b, WiFi.
Inmaculada Mora-Jiménez, Mark Wilby, Estrella Everss, Carlos Figuera, Javier Ramos-López, Alicia Guerrero-Curieses, José Luis Rojo-Álvarez, "Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location", IEEE Transactions on Mobile Computing, vol. 8, no. , pp. 1250-1264, September 2009, doi:10.1109/TMC.2009.37