Simultaneous sensor localization and target tracking in mine tunnels

Mine tunnels are extensive labyrinths with irregularly-shaped walls, in which a hundreds of employees are working on extraction of valuable ores and minerals. Since the working conditions are extremely hazardous, a (wireless) sensor network (WSN) is deployed to increase the safety in tunnels. One of the most important applications of WSN is to track the personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to the imprecise placement and/or possible sensor drops. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions (localization) and target tracking. We use a measurement model from a real mine, and apply a discrete variant of real-time belief propagation, which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties typical for mining environments. Comparing with standard Bayesian target tracking and localization algorithms, both the sensors' and the target's estimates are improved.