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_30: Accurate channel models are extremely important for the design of communications systems. Knowledge of the features of the channel provides communications system designers with the ability to predict the performance of the system for specific modulations, channel coding, and signal processing. This paper presents a statistical characterization of an Ultra-Wideband (UWB) propagation channel in an underground mine. Measurements were carried out in the 2---5 GHz frequency band. Various communication links were considered including both line-of-sight (LOS) and non-LOS (NLOS) scenarios. The measurement procedure allows us to characterize both the large-scale and the small-scale statistics of the channel. The aim here is to study in more details the statistical characteristics of the UWB propagation channel in an underground mine and to provide insight for future statistical channel modeling works. Channel characteristics examined include the distance and frequency dependency of path loss, shadowing fading statistics, and multipath temporal-domain parameter statistics such as the mean excess delay and the RMS delay spread. This work has been carried out by the underground communications research laboratory LRCS (The LRCS laboratory aims to develop research programs related to wireless telecommunications in underground mines. Research is conducted at its own facility as well as the CANMET experimental mine in Val-d'Or, Quebec, Canada), and the experimental mine CANMET (Canadian Center for Minerals and Energy Technology) in Val-d'or, Canada.
@cite_22: Wireless sensor networks (WSN) have attached a great attention in the last few years. WSN offer several advantages over the traditional sensor networks, such as elimination of costly wires, security and larger area coverage. In the last few years, there has been great interest commercial applications based on Ultra Wideband (UWB). It has potentially low complexity, low cost and an excellent time domain resolution which facilitates location and tracking applications. Therefore, UWB provide a good combination of high performance with low complexity for WSN applications. Localization has attracted considerable attention in many fields. Accurate estimation of an individual location can be a difficult task, producing ambiguous results particularly in harsh environment. In this paper, we focus on feasibility study (via simulation and measurements) of using UWB based-WNS as future solution for localization in underground mines.
@cite_17: The location of people, mobile terminals and equipment is highly desirable for operational enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflection, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest direct path between transmitter and receiver are the main sources of range measurement errors. Unreliable measurements of location metrics such as received signal strengths (RSS), angles of arrival (AOA) and times of arrival (TOA) or time differences of arrival (TDOA), result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the wide band neural network-locate (WBNN-locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90 and 80 of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface).
In @cite_22 @cite_30 , ultra-wideband (UWB) measurements were used for positioning. They were motivated by a high ranging accuracy in cluttered environments and low-cost implementation of the communication system. To solve the trilateration problem, many types of algorithms have been applied, including linearized least-squares, Gauss-Newton and bounding-box methods. The measurements were performed in the same environment as the one studied in @cite_17 . The main drawbacks of these algorithms are that the sensor nodes have to be precisely deployed and maintained, and that the algorithms are sensitive to outliers.