Download Free New Approach Of Indoor And Outdoor Localization Systems Book in PDF and EPUB Free Download. You can read online New Approach Of Indoor And Outdoor Localization Systems and write the review.

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization
Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains.
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.
This book provides a comprehensive and in-depth understanding of wireless indoor localization for ubiquitous applications. The past decade has witnessed a flourishing of WiFi-based indoor localization, which has become one of the most popular localization solutions and has attracted considerable attention from both the academic and industrial communities. Specifically focusing on WiFi fingerprint based localization via crowdsourcing, the book follows a top-down approach and explores the three most important aspects of wireless indoor localization: deployment, maintenance, and service accuracy. After extensively reviewing the state-of-the-art literature, it highlights the latest advances in crowdsourcing-enabled WiFi localization. It elaborated the ideas, methods and systems for implementing the crowdsourcing approach for fingerprint-based localization. By tackling the problems such as: deployment costs of fingerprint database construction, maintenance overhead of fingerprint database updating, floor plan generation, and location errors, the book offers a valuable reference guide for technicians and practitioners in the field of location-based services. As the first of its kind, introducing readers to WiFi-based localization from a crowdsourcing perspective, it will greatly benefit and appeal to scientists and researchers in mobile and ubiquitous computing and related areas.
Due to the widespread use of navigation systems for wayfinding and navigation in the outdoors, researchers have devoted their efforts in recent years to designing navigation systems that can be used indoors. This book is a comprehensive guide to designing and building indoor wayfinding and navigation systems. It covers all types of feasible sensors (for example, Wi-Fi, A-GPS), discussing the level of accuracy, the types of map data needed, the data sources, and the techniques for providing routes and directions within structures.
This book is a collection of best selected research papers presented at the International Conference on Communication and Artificial Intelligence (ICCAI 2020), held in the Department of Electronics & Communication Engineering, GLA University, Mathura, India, during 17–18 September 2020. The primary focus of the book is on the research information related to artificial intelligence, networks, and smart systems applied in the areas of industries, government sectors, and educational institutions worldwide. Diverse themes with a central idea of sustainable networking solutions are discussed in the book. The book presents innovative work by leading academics, researchers, and experts from industry.
Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap explores the state-of-the -art software tools and innovative strategies to provide better understanding of positioning and navigation in indoor environments using fingerprinting techniques. The book provides the different problems and challenges of indoor positioning and navigation services and shows how fingerprinting can be used to address such necessities. This advanced publication provides the useful references educational institutions, industry, academic researchers, professionals, developers and practitioners need to apply, evaluate and reproduce this book’s contributions. The readers will learn how to apply the necessary infrastructure to provide fingerprinting services and scalable environments to deal with fingerprint data. Provides the current state of fingerprinting for indoor positioning and navigation, along with its challenges and achievements Presents solutions for using WIFI signals to position and navigate in indoor environments Covers solutions for using the magnetic field to position and navigate in indoor environments Contains solutions of a modular positioning system as a solution for seamless positioning Analyzes geographical and fingerprint data in order to provide indoor/outdoor location and navigation systems
While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve the accuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. In particular, the book: Provides comprehensive coverage of the application of machine learning to the domain of indoor localization; Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization; Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.
"The rapid expansion of smartphones’ market coupled with the advances in mobile computing technology has opened up doors for new mobile services and applications. Quite a few of these services require the knowledge of the exact location of their handsets. Although, existing global positioning systems (GPS) perform best in outdoor environments, they have poor performance indoors. This has initiated the need for a new generation of positioning systems. In this thesis, we focus on wireless local area networks (WLAN)-based indoor positioning systems to act as GPS counterpart indoors. More specifically, we study two received signal strength (RSS)-based positioning techniques, fingerprinting and propagation models. We shed light on the advantages of each technique and propose different methods to counteract their shortcomings. Namely, we propose a hybrid solution of clustering and fast search techniques to reduce the computational requirements of fingerprinting. In addition, we propose a dimensionality reduction technique to restrict the location fingerprints to signal strength values received from only informative Access Points (APs), hence to further reduce fingerprinting complexity. For this purpose, we implement a modified fast orthogonal search method to choose the most informative APs from the set of all hearable APs in the region. Finally, we propose an indoor localization system that integrates the RSS correction methods to enhance the positioning accuracy of propagation models. This proposed system aims to achieve accurate modeling of signals’ propagation inside buildings without the need for expensive site surveys required for fingerprinting. Our experiments were conducted inside the engineering building at our university, using real RSS data. The obtained results show that the aforementioned first two proposed methods enhance fingerprinting techniques by reducing their computational complexity, while the third enhances the accuracy of propagation models."--Abstract.
The introduction of GPS-enabled mobile devices created in its wake a torrent of location-based applications and services. These devices are increasingly equipped with rich sensors for position and orientation, hearing, and vision, and by using their radios they can sense proximity to nearby people and devices. Considerable research over the past decades has demonstrated that these rich sensing platforms can also be used to provide reasonable location estimates in both indoor and outdoor environments. Research into the growing field of mobile (and indoor) localization has traditionally been concerned with providing scalable, cost-effective solutions for improving position estimation accuracy. Towards this end, researchers have explored RF-based ranging techniques, sensor fingerprinting methods, vision- and optics-based approaches, and inertial dead reckoning as potential mobile positioning technologies. Each of these methods presents a unique set of challenges---e.g., nonlinear and multipath RF propagation, training and infrastructure deployments, and sensor drift. Accompanying each of these challenges are overheads in the form of energy expenditure, increased computation, or increased wireless channel congestion. Reducing these overheads is a necessary step towards enabling indoor localization techniques to permeate the mobile market. This work focuses on methods for reducing the various overheads associated with localization by fusing position estimates with various low power sensing modalities, such as pressure, odometry, and even time. To do so, we distill localization routines into a spatial and temporal sequence of estimates and corresponding uncertainties. These estimates may be absolute, as is the case with GPS or fixed anchor nodes, or relative as with odometry and range. Each measurement is then treated as an edge in a graph whose nodes represent position estimates across time, allowing for joint optimization of multiple device locations across multiple sensor measurements and providing a method for easily integrating additional constraints into pre-existing positioning techniques. We divide this work into three thrusts as follows: (i) We present an architecture and formalization of collaborative indoor localization techniques as a single cohesive estimation routine. We leverage graph realization techniques as well as sensor fusion results from the sensor network literature to jointly optimize otherwise disjoint measurements, fusing absolute and relative measurements in a single optimizing routine; (ii) We explore wideband RF time-of-flight measurements as a method of providing accurate non-line-of-sight distance measurements and channel estimations between smartphones and other mobile devices, integrating these measurements to provide accurate, real-time error corrections for mobile positioning; and (iii) We construct a generic, open-source testbed upon which to analyze the algorithms and measurements employed, facilitating easier comparison and development of state-of-the-art positioning techniques and reducing the cost of obtaining measurements with corresponding ground truth observations.