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Über dieses Buch

Basics of Distributed and Cooperative Radio and Non-Radio Based Geolocation provides a detailed overview of geolocation technologies. The book covers the basic principles of geolocation, including ranging techniques to localization technologies, fingerprinting and localization in wireless sensor networks. This book also examines the latest algorithms and techniques such as Kalman Filtering, Gauss-Newton Filtering and Particle Filtering.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
The integration of location services into our day-to-day life will grow significantly over the next decade as technologies mature and accuracy improves. The evolution of localization technologies has occurred independently for different wireless systems/standards. The Global Positioning System (GPS) was the first system to bring to light the benefits of accurate and reliable location information. Consequently, it has been incorporated into many services and applications. Currently, outdoor localization, thanks to GPS, has revolutionized navigation-based applications running on automotive GPS-enabled devices and smart phones. Applications range from location awareness, to point-by-point directions between destinations, to identifying the closest cinema or coffee shop. The basic technology behind the system is to measure the time elapsed for a signal to travel between a number of satellites orbiting the globe and a mobile device. Through a computational technique known as triangulation, the location of the mobile can be calculated from the tracked positions of the satellites and the times measured, each known as the Time-of-Arrival. The success of GPS has been due to the reliability, availability, and practical accuracy that the system can deliver; however, GPS lacks coverage indoors and in urban areas, in particular near buildings when the signal is blocked; even in the best of conditions, the accuracy is on the order of several meters.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 2. Ranging and Localization in Harsh Multipath Environments

Abstract
In this chapter, we will first introduce the basics of geolocation techniques that are based on Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), and Received Signal Strength (RSS). Then we introduce the major challenges to accurate localization: multipath propagation and non-line-of-sight conditions where we will focus on the two most popular ranging techniques, TOA and RSS, and evaluate how the accuracy of localization is affected by these physical challenges. We will further highlight the relationship between the accuracy of estimation and the signal to noise ratio and bandwidth parameters through the well-known Cramer-Rao Lower Bound (CRLB) equations. Finally, we will introduce measurement and modeling of the RSS/TOA ranging that will highlight the impact of multipath and NLOS on the accuracy of ranging systems.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 3. Multipath and NLOS Mitigation Algorithms

Abstract
In Chap. 2, the multipath and NLOS problems were introduced and the degrading impact on distance estimation was highlighted through channel measurements and modeling. In this chapter, we will first introduce popular multipath mitigation techniques and then highlight the major approaches to dealing with the NLOS problem. For the multipath problem two mitigation techniques will be introduced: Super-resolution algorithms and Ultra Wideband (UWB) technology. The former is a spectral estimation technique that improves the TOA estimation through enhancing the time-domain resolution. The latter approach is an emerging technology that transmits very narrow pulses in time (very large bandwidths) and thus has the benefit of improved time-domain resolution which results in higher TOA estimation accuracy. The second part of the chapter is dedicated to NLOS identification and mitigation algorithms. An important pre-requisite to NLOS mitigation is channel identification. The effectiveness of the mitigation algorithms will rely mainly on the accuracy of NLOS channel identification. Thus, we will first introduce popular approaches to NLOS identification and then conclude the chapter with NLOS mitigation algorithms.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 4. Survey-Based Location Systems

Abstract
Location fingerprinting systems can be differentiated for the most part by the following two characteristics: (1) the feature selected to fingerprint the sites; and (2) the mapping algorithm to determine the mobile’s location. In this chapter, we introduce several fingerprinting techniques. Given its prevalence, we concentrate on the RSS feature in the first part of the chapter. The same techniques, however, apply to other features as well. In the first section, an analytical model of a generic fingerprinting system is presented. The model describes how the salient parameters common to most systems affect their performance. The subsequent section showcases a number of methods to compute the similarity metric for memoryless systems—that is—systems which estimate location based on readings taken at a single time instant. Section 4.3 introduces systems with memory and shows how maintaining some historic path data can enhance location precision significantly. In the remainder of the chapter, we introduce some non-RSS features. Section 4.4 investigates the use of the channel impulse response as an alternative radio frequency signature. Conversely, Sect. 4.5 reports on non-RF features altogether—features which are available from devices such as smartphones, namely sound, motion, and color.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 5. Cellular Localization

Abstract
Mobile network operators offer location-based services for their customers as well as for third party customers that support such applications. The high density of mobile users in urban and indoor environments drive the need for such location-based services, especially in areas that are GPS-denied. Providing location services for mobile devices, such as position tracking, is an important objective for cellular network operators. Example applications for which location information is critical include:
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 6. Cooperative Localization in Wireless Sensor Networks: Centralized Algorithms

Abstract
The basic localization techniques known as triangulation and angulation were introduced in Chap.​ 2. In two-dimensional triangulation the location of a mobile device is computed by measuring its range from at least three base stations with known coordinates. Analogously, in two-dimensional angulation the mobile’s location is computed from the arrival angle from at least two stations. While these techniques are practical in Global Positioning Systems or cellular networks, in some networks connectivity of some nodes to even two base stations cannot be guaranteed. A prime example is in wireless sensor networks (WSNs). Because wireless sensors often have a deployment life of months or even years, battery conservation is critical to their operation. This prescribes transmitting infrequently and over short distances. To address the latter, nodes communicate between each other via short, multihop links to stations external to the network (Perkins 2001). The coordinates of the base stations are either hardwired or—because in most cases they are installed outside—can be determined through GPS. In contrast, many WSN applications—such as military or in emergency response—require on-the-fly setup, meaning sensor positions cannot be hardwired and, since sensors are battery constrained, they may not have sufficient power to receive GPS signals. As such, sensors must cooperate in order to extrapolate their locations through multihop links to the stations. This is the basis of cooperative localization.
Camillo Gentile, Nayef Alsindi, Ronald Alsindi, Carole Teolis

Chapter 7. Cooperative Localization in Wireless Sensor Networks: Distributed Algorithms

Abstract
Chapter 6 introduces centralized algorithms for cooperative positioning. In this chapter, we focus on distributed algorithms applied to cooperative positioning. Distributed algorithms differ on where and when information is processed. Centralized algorithms first collect all potential information at a central unit and then process the data (Figueiras 2008). Compared to this serial methodology, the distributed algorithms process data in parallel at different units. Furthermore, distributed algorithms rely on the connections between geographically distributed sensor nodes for the mutual exchange of information.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 8. Inertial Systems

Abstract
Chapters 8 is intended to provide an introduction to the use of inertial sensors as part of a solution for GPS denied navigation system. An inertial navigation systems (INS) is a navigation system that provides position, orientation, and velocity estimates based solely on measurements from inertial sensors. Inertial measurements are differential measurements in the sense that they quantify changes in speed or direction. The two primary types of inertial sensors are accelerometers and gyroscopes. These sensors discussed have complementary error characteristics to RF sensors and so can enable mitigation of the effects of multipath and NLOS errors in the location solution. A main drawback of using purely inertial systems for navigation is that errors in the differential measurements are necessarily accumulated in the navigation solution over time. Thus, even with highly precise inertial measurements, position estimates based on them degrade over time. The key to making inertial sensors part of a precision positioning system is developing methods to both minimize free inertial position error growth and bound accumulated inertial position errors. It is now well accepted that a high accuracy navigation solution requires the ability to fuse input from multiple sensors making use of all available navigation information. In Chapter 8 we discuss fusion of inertial sensor data with sensors and/or algorithms that provide estimates of secondary inertial state variables such as velocity, heading, and elevation.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Chapter 9. Localization and Mapping Corrections

Abstract
Chapter 9 gives an overview of localization and mapping with a focus on near real-time implementation. We look at sensors that provide information about the environment (allothetic) and that aid us in creating a map of what is around us. The created map is also used for localization. This classic problem of simultaneous localization and mapping (SLAM) requires fusion of information from idiothetic and allothetic sensors. The basic idea of SLAM is that if the sensor and algorithms can identify a landmark and a location of that landmark relative to tracked subject, then any time that landmark is seen again, its location can be used to correct the tracked subject’s location. We discuss a small set of environmental sensors that can be used in SLAM algorithms including optical, magnetometer an inertial and discuss how features are selected. We give an overview of approaches to solving the SLAM problem and then show some results of a particular implementation.
Camillo Gentile, Nayef Alsindi, Ronald Raulefs, Carole Teolis

Backmatter

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