RSS-based localization of a moving node in 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 homogeneous, i.e., the attenuation factors of the various links are one and the same while the transmitted powers are different. The blind node is moving along a trajectory that is unknown. We propose a two-stage procedure: the first stage exploits measurements between anchors, i.e., nodes of known position, to compute the ML estimate of the transmitted powers and the attenuation factor. Then, a ML solution, using measurements at the blind node only, is proposed to estimate the unknown trajectory, based upon the Viterbi algorithm. The performance assessment, carried out also in comparison to other algorithms, shows that the proposed approach could be a viable means to handle localization of moving nodes in uncertain scenarios.