To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track 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 imprecise placement and or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation that can efficiently solve this high-dimensional problem and handle all non-Gaussian uncertainties expected in this kind of environment. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms and provides robustness against outliers.
@cite_8: Improvements in underground wireless tracking technology are critical for improved safety and productivity in underground mines. While the recent focus on technology development has been for underground coal mines, improvements are required for block caving operations which have very different characteristics from coal mines. Block caving is characterized by a large number of draw points from which the mineral ore is extracted from under the ore body, and efficient and safe operation of such a mine requires careful monitoring and control of the distribution of ore extraction from the draw points. In this paper a new algorithm is presented for accurately tracking mine vehicles in an underground mine with a low density of reference nodes. A system is described that was installed at an operation mine, and results from a year long trial are presented. The results showed that the system is capable of accurately tracking ore extraction in an operational mine, and of reliable operation over an extended period of time.
In @cite_8 , Bayesian point-mass (grid-based) filtering was applied to track mine vehicles. The main goal was monitoring and control of ore extraction from the draw points in a mine in Australia. Since the draw points are very close to each other, high tracking accuracy is required. The main problem of this approach is that it requires many grid points in order to obtain sufficiently accurate estimates.