On-Line Training of the Path-Loss Model in Bayesian WLAN Indoor Positioning

Abstract

Received signal strengths have been widely used in indoor positioning due to the massive presence of wireless local networks in buildings. To avoid long training phases theoretical propagation models such as the path-loss model can be used. The main issue is that the path-loss parameters, namely the transmitted power and the decay exponent, can assume a wide range of values, depending on devices, building structure and other environmental features. In this paper, we propose a Bayesian positioning algorithm based on the Rao-Blackwellized particle filter, where the system estimates the parameters of the path-loss model independently for each AP in addition to localizing the user. Both parameters are described by discrete variables and their probability distribution is estimated starting from a uniform prior. We validate the algorithm with simulations and two different experiments; finally, some remarks on complexity are also given.