On the evaluation of geometric localization using Recursive Maximum Likelihood estimation

In this paper, we propose a novel positioning algorithm based on hybrid cooperative techniques. The proposed algorithm is divided into two main categories: initial positioning and tracking. After acquiring an estimate about the distances of an un-located mobile terminal (UMT) by measuring the Received Signal Strength (RSS) and Time of Arrival (ToA), we obtain initial position for an UMT via triangulations and Recursive Maximum Likelihood (ML) estimator. The recursive estimation is achieved by dividing the studied region into a grid of possible locations. After having initial estimated measured positions at different time stamps, those positions can be enhanced via an hybrid combination through the Extended Kalman Filter (EKF) proposed in this work for tracking. Simulation results show that the proposed positioning technique performs very well, even in shadowed regions.