On-line training of the path-loss model in Bayesian WLAN indoor positioning

Received signal strengths have been widely exploited in indoor positioning due to the massive presence of wireless local networks in buildings. Theoretical propagation models such as the path-loss model can be used in order to avoid long training phases as in fingerprinting approaches. The main issue concerning the employiment of the path-loss model is that the values of some parameters, i.e., the transmit power and the decay exponent, depend on many factors, such as the device, building structure and other environmental features. In this paper, we propose a Bayesian positioning algorithm based on the Rao-Blackwellized particle filter, where the parameters of the path-loss model are estimated independently for each AP in addition to localizing the user. Both parameters are described by discrete random variables with uniform priors. We validate ou proposal by means of simulations and two different experiments; finally, some remarks on complexity are also given.