This article is part of the series Signal Processing for Location Estimation and Tracking in Wireless Environments.
Research Article
Environment-Aware Location Estimation in Cellular Networks
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* Corresponding author: Gürkan Gür gurgurka@boun.edu.tr
Satellite Networks Research Laboratory (SATLAB), Department of Computer Engineering, Boğaziçi University, 34342 Bebek, Istanbul, Turkey
EURASIP Journal on Advances in Signal Processing 2008, 2008:276456 doi:10.1155/2008/276456
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/276456
| Received: | 23 March 2007 |
| Revisions received: | 26 August 2007 |
| Accepted: | 19 December 2007 |
© 2008 The Author(s).
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
We propose a novel mobile positioning algorithm for cellular networks based on the estimation of the radio propagation environment. Since radio propagation characteristics vary in different environments, knowing the environment of the mobile user is essential for accurate Received Signal Strength- (RSS-) based location estimation. The key feature of our method is its capability to estimate the environment of the mobile user using machine learning techniques and to utilize this information for enhancing RSS-based distance calculations. The proposed algorithm, namely, EARBALE, has been evaluated using field measurements collected from a GSM network in diverse geographic locations. Our approach turns out to be significantly beneficial, enhancing estimation accuracy, and thereby enabling high-performance mobile positioning in a practical and cost-effective manner. Additionally, it is computationally light-weight and can be integrated onto any RSS-based algorithm as an enhancement add-on.
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