RSS Based Localization by Using Lognormal Mixture Shadowing Model

Abstract

Performance of received signal strength (RSS) based localization techniques are directly related to the employed RSS model. Hence, localization performance can be enhanced by improving the accuracy of the RSS model. Lognormal mixture shadowing model for wireless channels, generated by taking distinct scattering clusters into account, characterizes RSS variable more accurately than classical lognormal shadowing model. In this paper, lognormal mixture is applied to localization techniques by means of the derived maximum likelihood estimator. Through simulations performed making use of an exemplary microcellular network structure, it is demonstrated that the mixture model significantly increases the performance of RSS based localization systems compared to the classical model.