RSS-based localization in non-homogeneous environments

In this paper, we deal with the problem of RSS-based self-localization of a wireless blind node using a statistical path loss model for the measurements. The considered environment is non-homogeneous, i.e., the attenuation factors of the various links are different. We propose a two-stage procedure: the first stage exploits measurements between anchors to estimate transmitted powers and attenuation factors. Then, a ML localization algorithm, fed by the measurements at the blind node only, is used to estimate the unknown position. In this second stage, the attenuation factors between the blind node and the anchors are modeled as IID RVs ruled by a Gaussian distribution with mean and variance to be computed based on the estimated attenuation factors of the first stage. The performance assessment shows that the proposed approach could be a viable means to handle localization in non-homogeneous environments.