You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: In this paper, we propose a WiFi-based stop notification system. The users of the system inform their destination stops to the system, then the system provides subway navigation services to the users in real-time. To construct the system, we have collected WiFi fingerprints at 15 lines for 536 Seoul subway stops. Also, we have implemented a WiFi-based subway stop notification system for Seoul subways in Korea. We have tested the accuracy of our system, Nerimi, at Seoul subways. Among the 536 stops, the system could detect the subway stops with over 90 accuracy. Thus, if we can capture the WiFi signals at every stop of the subways, using the WiFi signals will be highly eligible for building a subway navigation system.

[3]: Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a Structure-Aware Self-Adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. We further develop a sound and robust mechanism for efficiently handling queries under instable circumstances. A prototype is deployed with 27 mica2 motes in a real coal mine. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments.

[4]: 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.

[5]: 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.

[6]: Recent mine disasters in the United States exposed various inadequacies and gaps in mine-safety technology. Congress responded to these tragedies by enacting the Mine Improvement and New Emergency Response Act of 2006 (MINER Act), which resulted in the most significant change to mine-safety laws in 30 years. To help meet the requirements of the MINER Act and to help eliminate future tragedies, the National Institute for Occupational Safety and Health (NIOSH) initiated aggressive research programs for developing new, and enhancing existing, mine-safety technologies. A major research emphasis addressed the lack of post- accident communications, which surfaced as a critical deficiency in some disasters. As a result, three communications approaches emerged as viable technologies - enhanced leaky-feeder, wireless-mesh, and medium-frequency systems. This paper describes the operation, application, advantages, and disadvantages of each system, as well as the challenges associated with underground wireless communications.

[7]: 

[8]: 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.

[9]: 

[10]: 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).
Contemporary techniques for localization and tracking in confined environments are very basic. They are typically based on manual reporting of the employee's location using paging phones or video surveillance [4] [6] . Moreover, there are few proposals in the literature, based on fingerprinting [10] [9] [8] [2] , trilateration [4] [1] , centroid [3] and Bayesian filtering [5] [7] .