Statistical path loss parameter estimation and positioning using RSS measurements

An efficient Bayesian method for off-line estimation of the position and the path loss model parameters of a base station is presented. Two versions of three different on-line positioning methods are tested using real data collected from a cellular network. The tests confirm the superiority of the methods that use the estimated path loss parameter distributions compared to the conventional methods that only use point estimates for the path loss parameters. Taking the uncertainties into account is computationally demanding, but the Gauss-Newton optimization methods is shown to provide a good approximation with computational load that is reasonable for many real-time solutions.