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In this article, we propose a novel approach to solving the localization problem in cellular networks, with a focus on indoor environments. The goal is to estimate a mobile user's position, based on measurements of signals received from different base stations. Our solution uses the Progressive Bayes estimation framework to model the distribution of signal measurements, as obtained in a series of calibration measurements. In the localization step, we compute the full probability density over the user position. We also show that user motion models can be easily integrated in our solution.

at - automatisierungstechnik

Data-Driven Modeling of Signal Strength Distributions for Localization in Cellular Radio Networks (Datengetriebene Modellierung von Feldstärkeverteilungen für die Ortung in zellulären Funknetzen)