Dynamic path-loss estimation using a particle filter

The estimation of the propagation model parameters is a main issue in location systems. In these systems, distance estimations are obtained from received signal strength information, which is extracted from received packets. The precision of these systems mainly depends on the proper propagation model selection. In this paper we introduce an algorithm based on Bayesian filtering techniques, which estimate the path-loss exponent of a lognormal propagation model. This estimation is made dynamically and in real time. Therefore, it can track propagation model changes due to environmental changes.