Optimization Algorithms in Local and Global Positioning

Optimization Algorithms in Local and Global Positioning

Jean-Philippe Montillet, Kegen Yu, Lukasz Kosma Bonenberg, Gethin Wyn Roberts
DOI: 10.4018/978-1-4666-9644-0.ch001
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Abstract

With the rise of large city and the need of large civil engineering structures and city planning, surveying industry improves continuously their instruments/software in order to get cm accuracy position anywhere. Moreover, since the boom of mobile phones in the late 90s, location has become very valuable information for security, emergency and commercial applications. Depending of the application, the location technologies vary based on the accuracy of the location and the price of the system, which delivers the location information to the user. For outdoor applications, Global Navigation Satellite System is the main candidate, whereas if the user/mobile node is indoors or in a narrow street other technologies will be preferred such as the ones based on Wi-Fi or radio-frequency signal.This chapter provides an overview of different positioning technologies used in geo-location together with their limits/advantages. This chapter studies also a number of algorithms developed to estimate the position coordinates of a static or mobile user or target.
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Background

Positioning and localization has been an active and important area of research especially in electrical engineering, computer engineering and civil engineering with a particular emphasis in surveying. Galileo Galilei’s work of triangulating the position of a ship using the positions of the stars in the 17th century can be seen as a pioneering work in this area (Singer, 1941). Large efforts were focused on emerging radio navigation with the first antennas manufactured in the late 1800. Subsequent improvements in radio communications and the discovery of radar systems in the 1930’s accentuated further the need to locate ships and planes (Balanis, 2012). In 1978, a second revolution started with the advent of Global Positioning System (GPS) motivated by the US army to get a position of military vehicles and ships anywhere at anytime on Earth thanks to the emerging satellite technology (Strang & Borre, 1997).

Key Terms in this Chapter

Tracking: In the field of navigation and tracking, it is the action of a system to keep track of vehicles or more generally mobile nodes in real-time. This action involves location technologies (i.e. terrestrial based and/or satellite-based technologies) and GIS.

Positioning Accuracy: error on the estimated position of a receiver related to the true position. The positioning accuracy is the results of various phenomena (e.g., signal propagation through different layers of the atmospheres, NLOS between transmitters and receiver, multipath, geophysical processes, …). For many applications in satellite based precise positioning and especially in the application to geodesy, the study of positioning accuracy includes the broad field of time series analysis (e.g. parameters estimation, stochastic modeling, …).

Global Navigation Satellite System (GNSS): generic name about the various satellite constellations: Global Positioning System (USA), GLONASS (Russia), BeiDou (China) and Galileo (European Union).

Location Based Services (LBS): general class of computer program-level services that use location data. It gathers user applications linked to various contexts such as health, indoor object search, entertainment, work, and personal life.

Localization: generic term which is used to describe the algorithms and technologies to estimate the position of a user or node. The field of localization is broad and includes satellite systems, navigation systems, indoor and outdoor terrestrial based technologies.

Navigation: filed of study that focuses on the process of monitoring and controlling the movement of a craft or vehicule from one place to another. This field is broad and includes land, marine, aeronautic and space navigation (i.e. satellite systems).

Adaptive Algorithm: algorithm that changes its behavior based on the information available at the time it is running. This field includes the families of least mean squares, recursive least mean squares, and Kalman filters.

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