Target Tracking in Confined Environments with Uncertain Sensor Positions

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 which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. 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.Comment: IEEE Transactions on Vehicular Technology, 201

Precise positioning systems for Vehicular Ad-Hoc Networks

Vehicular Ad Hoc Networks (VANET) is a very promising research venue that can offers many useful and critical applications including the safety applications. Most of these applications require that each vehicle knows precisely its current position in real time. GPS is the most common positioning technique for VANET. However, it is not accurate. Moreover, the GPS signals cannot be received in the tunnels, undergrounds, or near tall buildings. Thus, no positioning service can be obtained in these locations. Even if the Deferential GPS (DGPS) can provide high accuracy, but still no GPS converge in these locations. In this paper, we provide positioning techniques for VANET that can provide accurate positioning service in the areas where GPS signals are hindered by the obstacles. Experimental results show significant improvement in the accuracy. This allows when combined with DGPS the continuity of a precise positioning service that can be used by most of the VANET applications.Comment: 15 pages, 15 figures, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 4, No. 2, April 201

Communication technologies to design vehicle-to-vehicle and vehile-to-infrastructures applications

Intelligent Transport Systems use communication technologies to offer real-time traffic information services to road users and government managers. Vehicular Ad Hoc Networks is an important component of ITS where vehicles communicate with other vehicles and road-side infrastructures, analyze and process received information, and make decisions according to that. However, features like high vehicle speeds, constant mobility, varying topology, traffic density, etc. induce challenges that make conventional wireless technologies unsuitable for vehicular networks. This paper focuses on the process of designing efficient vehicle-to-vehicle and vehicle-to road-side infrastructure applications.Peer ReviewedPostprint (published version

Cognitive Radio Connectivity for Railway Transportation Networks

Reliable wireless networks for high speed trains require a significant amount of data communications for enabling safety features such as train collision avoidance and railway management. Cognitive radio integrates heterogeneous wireless networks that will be deployed in order to achieve intelligent communications in future railway systems. One of the primary technical challenges in achieving reliable communications for railways is the handling of high mobility environments involving trains, which includes significant Doppler shifts in the transmission as well as severe fading scenarios that makes it difficult to estimate wireless spectrum utilization. This thesis has two primary contributions: (1) The creation of a Heterogeneous Cooperative Spectrum Sensing (CSS) prototype system, and (2) the derivation of a Long Term Evolution for Railways (LTE-R) system performance analysis. The Heterogeneous CSS prototype system was implemented using Software-Defined Radios (SDRs) possessing different radio configurations. Both soft and hard-data fusion schemes were used in order to compare the signal source detection performance in real-time fading scenarios. For future smart railways, one proposed solution for enabling greater connectivity is to access underutilized spectrum as a secondary user via the dynamic spectrum access (DSA) paradigm. Since it will be challenging to obtain an accurate estimate of incumbent users via a single-sensor system within a real-world fading environment, the proposed cooperative spectrum sensing approach is employed instead since it can mitigate the effects of multipath and shadowing by utilizing the spatial and temporal diversity of a multiple radio network. Regarding the LTE-R contribution of this thesis, the performance analysis of high speed trains (HSTs) in tunnel environments would provide valuable insights with respect to the smart railway systems operating in high mobility scenarios in drastically impaired channels

Reliable localization methods for intelligent vehicles based on environment perception

Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport Systems (ITS) as a potential future of transportation. Today, thanks to all the technological advances in recent years, the feasibility of such systems is no longer a question. Some of these autonomous driving technologies are already sharing our roads, and even commercial vehicles are including more Advanced Driver-Assistance Systems (ADAS) over the years. As a result, transportation is becoming more efficient and the roads are considerably safer. One of the fundamental pillars of an autonomous system is self-localization. An accurate and reliable estimation of the vehicle’s pose in the world is essential to navigation. Within the context of outdoor vehicles, the Global Navigation Satellite System (GNSS) is the predominant localization system. However, these systems are far from perfect, and their performance is degraded in environments with limited satellite visibility. Additionally, their dependence on the environment can make them unreliable if it were to change. Accordingly, the goal of this thesis is to exploit the perception of the environment to enhance localization systems in intelligent vehicles, with special attention to their reliability. To this end, this thesis presents several contributions: First, a study on exploiting 3D semantic information in LiDAR odometry is presented, providing interesting insights regarding the contribution to the odometry output of each type of element in the scene. The experimental results have been obtained using a public dataset and validated on a real-world platform. Second, a method to estimate the localization error using landmark detections is proposed, which is later on exploited by a landmark placement optimization algorithm. This method, which has been validated in a simulation environment, is able to determine a set of landmarks so the localization error never exceeds a predefined limit. Finally, a cooperative localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle detections in order to enhance the estimation provided by GNSS systems. Multiple experiments are carried out in different simulation environments to validate the proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes del Transporte (ITS) se veían como un futuro para el transporte con gran potencial. Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada vez más eficiente y las carreteras son considerablemente más seguras. Uno de los pilares fundamentales de un sistema autónomo es la autolocalización. Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial para la navegación. En el contexto de los vehículos circulando en exteriores, el Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante. Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los cambios en el entorno pueden provocar cambios en la estimación, lo que los hace poco fiables en ciertas situaciones. Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar los sistemas de localización en vehículos inteligentes, con una especial atención a la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones: En primer lugar, se presenta un estudio sobre cómo aprovechar la información semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la contribución de cada tipo de elemento del entorno a la salida de la odometría. Los resultados experimentales se han obtenido utilizando una base de datos pública y se han validado en una plataforma de conducción del mundo real. En segundo lugar, se propone un método para estimar el error de localización utilizando detecciones de puntos de referencia, que posteriormente es explotado por un algoritmo de optimización de posicionamiento de puntos de referencia. Este método, que ha sido validado en un entorno de simulación, es capaz de determinar un conjunto de puntos de referencia para el cual el error de localización nunca supere un límite previamente fijado. Por último, se propone un algoritmo de localización cooperativa basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método propuesto ha sido validado mediante múltiples experimentos en diferentes entornos de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño