A Probabilistic Approach to WLAN User Location Estimation

We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.

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