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_25: This paper presents a novel method to reduce the recalibration costs of a radio map by automatically updating the radio map. The appearance frequencies of access points (APs) detected from user feedback data are mainly used for the update. The proposed method appeared superior to previous methods, especially in its ability to update newly installed APs in the radio map. According to the experiment conducted for the radio map of 233 Seoul subway stops, the proposed method was effective for updating APs with weak as well as strong signal strengths.
@cite_39: Localizing people in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery tunnel suffer from severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting channel impulse response (CIR) fingerprints with reference to one wireless receiver and using an artificial neural network as a matching algorithm to localize. In this article we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints will allow us to create artificial neural networks that will work separately or cooperatively using the same localization technique. The results will show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points improves significantly the accuracy, precision, scalability and the overall performance of the localization system.
@cite_3: 
@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).
More specifically, in @cite_17 , a fingerprinting technique was proposed, in which seven relevant parameters (including mean excess delay, total received power, and delay spread) were learned offline from wideband impulse responses measured at hundreds of locations. Then, these 7D vectors were used as the input to an artificial neural network pattern-matching algorithm. The measurements were conducted in a gallery of the CANMET mine, a former gold mine located in Quebec, Canada. This method was then improved in @cite_39 by using more receivers with known positions. The fingerprinting techniques @cite_25 @cite_3 , based on WiFi signals, have been also applied in subway tunnels in Seoul, S. Korea. The main problem of these algorithms is that they are not well suited for dynamic propagation environments (e.g., caused by movement of heavy machinery) in which the fingerprints have to be updated very frequently.