A Low-Complexity Iterative Message Passing Algorithm for Robust RSS-TOA IoT Localization

This contribution considers the problem of robust target localization using the possibly unreliable hybrid received signal strength and time-of-arrival measurements, in the Internet of Things (IoT) context. Traditional positioning approaches relying on either extra a priori error information for robustification or the computationally intensive convex programming techniques for optimization do not fit well into the IoT applications with limited computing resources. Such concerns, however, will jeopardize the straightforward applicability of many ready-made solutions to the IoT positioning services if left untreated. In this article, the problem is resolved in a different manner. We adopt here a Geman–McClure like loss function, which is much less sensitive to the biased sensor observations, in order to statistically robustify the $\ell _{2}$ -sapce-based location estimator. A computationally attractive iterative message passing algorithm is then developed to conduct efficient optimization. Simulation results demonstrate the performance superiority of the proposed scheme over its competitors in various localization environments.