40 research outputs found

    A loose-coupled fusion of inertial and UWB assisted by a decision-making algorithm for localization of emergency responders

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    Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs). These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision-making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non-line-of-sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time-of-arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.This work has been partially supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and Project UID/CTM/00264/2019 of 2C2T - Centro de Ciência e Tecnologia Têxtil, funded by National Founds through FCT/MCTES. The work of A. G. Ferreira and D. Fernandes was supported by the FCT under Grant SFRH/BD/91477/2012 and Grant SFRH/BD/92082/2012

    Identification & Mitigation of NLOS Information for UWB based Indoor Localization

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    Technology advancements such as GPS, automation and robotics have completely changed the world and produced new industries, once thought to be unimaginable a century ago. As with all technology, these systems come with limitations and can be further improved. At this time, all of these systems share one common problem; they cannot work together in an indoor environment. The advent of indoor positioning systems aims to create a union between these technologies such as allowing robots to be location aware. Indoor positioning is currently a new technology with no defined standard. Ultra-wideband based indoor positioning systems have become popular because of their resistance to multipath and high resolution due to a large bandwidth. The Ultra-wideband based system in this thesis utilizes the time of arrival technique to calculate distances and thus a user’s position. Time of arrival is only reliable when there is a line-of-sight between two transceivers. If there is no line-of-sight, the distances calculated are inaccurate thus impacting the accuracy of a user’s position. This thesis proposes a practical, non-hardware intensive solution to identify if there is a no line-of-sight condition and mitigates the measured range between a tag and the anchor nodes. Line-of-sight identification was implemented using the channel impulse response data. Ranging and positioning mitigation was achieved using a geometric based mitigation scheme. An accuracy of 90% was achieved for the identification of no line-of-sight and an improvement factor of 2.81 was achieved for the calculated mitigated position of a tag

    Recent Advances in Indoor Localization Systems and Technologies

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    Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

    Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations

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    Zandian R. Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations. Bielefeld: Universität Bielefeld; 2019.This thesis discusses the utilization of ultra-wideband (UWB) technology in indoor localization scenarios and proposes system setup and evaluates different localization algorithms in order to improve the localization accuracy and stability of such systems in non-ideal conditions of the indoor environment. Recent developments and advances of technology in the areas of ubiquitous Internet, robotics and internet of things (IoT) have resulted in emerging new application areas in daily life in which localization systems are vital. The significant demand for a robust and accurate localization system that is applicable in indoor areas lacking satellites link, can be sensed. The UWB technology offers accurate localization systems with an accuracy of below 10 cm and covering the range of up to a few hundred meters thanks to their dedicated large bandwidth, modulation technique and signal power. In this thesis, the technology behind the UWB systems is discussed in detail. In terms of localization topologies, different scenarios with the focus on time-based methods are introduced. The main focus of this thesis is on the differential time of arrival localization systems (TDoA) with unilateral constellation that is suitable for robotic localization and navigation applications. A new approach for synchronization of TDoA topology is proposed and influence of clock inaccuracies in such systems are thoroughly evaluated. For localization engine, two groups of static and dynamic iterative algorithms are introduced. Among the possible dynamic methods, extended Kalman filter (EKF), H∞ and unscented Kalman filter (UKF) are discussed and meticulously evaluated. In order to tackle the non-line of sight (NLOS) problem of such systems, for detection stage several solutions which are based on parametric machine learning methods are proposed. Furthermore, for mitigation phase two solutions namely adjustment of measurement variance and innovation term are suggested. Practical results prove the efficiency and high reliability of the proposed algorithms with positive NLOS condition detection rate of more than 87%. In practical trials, the localization system is evaluated in indoor and outdoor arenas in both line of sight and non-line of sight conditions. The results show that the proposed detection and mitigation methods can be successfully applied for both small and large-scale arenas with the higher performance of the localization filters in terms of accuracy in large-scale scenarios

    Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Indoor Positioning and Navigation

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    In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

    Cooperative Radio Communications for Green Smart Environments

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    The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

    Multipath assisted positioning using machine learning

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    The multipath propagation of the radio signal was considered a problem for positioning systems that had to be eliminated. However, a groundbreaking new approach called multipath assisted positioning caused a paradigm shift, where multipath propagation improves the positioning performance. Moreover, the multipath assisted positioning algorithm called Channel-SLAM shows the possibility of using a single physical transmitter in a multipath environment for positioning. In this thesis, I open a discussion on some problems that have vital importance for multipath assisted positioning algorithms with a focus on pedestrian positioning. Using the idea of multipath assisted positioning, I present a single frequency network positioning algorithm. I evaluated the single frequency network-based positioning algorithm for positioning in a real scenario using a terrestrial digital video broadcasting transmission. I propose a novel pedestrian transition model utilizing the inertial measurements from a handheld inertial measurement unit. The proposed pedestrian transition model improves the precision and reliability of the Channel-SLAM. Comparing the proposed transition model with the Rician transition model previously used in Channel-SLAM quantifies the performance improvement. This thesis proposes a joint data association technique that overcomes the strong dependence on the radio channel estimation algorithm used in Channel-SLAM. The joint data association allows reusing the previously observed virtual transmitters after an outage of multipath component tracking. The evaluation based on the walking pedestrian scenario shows that the joint data association algorithm provides superior positioning precision. The virtual transmitter position estimation yields a significant computational load in Channel-SLAM. I propose a method that represents the virtual transmitter by a Gaussian mixture model and learns its parameters. The evaluation shows that the proposed method outperforms the previous approach while decreasing the computational load. Also, the current methods for radio channel estimation yield a considerable computational load that prohibits a real-time deployment. The thesis investigates the possibility of using artificial neural networks trained to estimate the number of multipath components and corresponding delays in a noisy measurement of a channel impulse response. The artificial neural network-based delay estimator provides a superresolution performance and faster runtime than the classical approaches. The precision of the trained artificial neural network architecture is evaluated and compared to the Cramer-Rao lower bound theoretical limit and classical channel estimation algorithms

    Cooperative Radio Communications for Green Smart Environments

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    The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin
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