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2013 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

verfasst von: Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration is an introduction to the field of Integrated Navigation Systems. It serves as an excellent reference for working engineers as well as textbook for beginners and students new to the area. The book is easy to read and understand with minimum background knowledge. The authors explain the derivations in great detail. The intermediate steps are thoroughly explained so that a beginner can easily follow the material. The book shows a step-by-step implementation of navigation algorithms and provides all the necessary details. It provides detailed illustrations for an easy comprehension.

The book also demonstrates real field experiments and in-vehicle road test results with professional discussions and analysis. This work is unique in discussing the different INS/GPS integration schemes in an easy to understand and straightforward way. Those schemes include loosely vs tightly coupled, open loop vs closed loop, and many more.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
The word ‘navigation’ comes from Latin navigare which means ‘to sail’. The word navigare itself is derived from navis, which stands for ‘ship’, and agere, meaning ‘to drive’ (Esmat Sep 2007). Early navigation primarily dealt with vessels traveling in sea. However, it has now permeated into every imaginable form of transportation as well as various other applications including location-based services, search and rescue, law enforcement, road and air travel, remote asset tracking, fleet management, intelligence gathering, sports, public safety, and environmental assessment and planning (El-Rabbany 2002). Advances in microelectronics and miniaturization of integrated circuits have facilitated the production of inexpensive inertial sensors, global positioning system (GPS) receivers and powerful computers. This has placed navigation systems within easy reach of low cost applications.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 2. Basic Navigational Mathematics, Reference Frames and the Earth’s Geometry
Abstract
Navigation algorithms involve various coordinate frames and the transformation of coordinates between them. For example, inertial sensors measure motion with respect to an inertial frame which is resolved in the host platform’s body frame. This information is further transformed to a navigation frame. A GPS receiver initially estimates the position and velocity of the satellite in an inertial orbital frame. Since the user wants the navigational information with respect to the Earth, the satellite’s position and velocity are transformed to an appropriate Earth-fixed frame. Since measured quantities are required to be transformed between various reference frames during the solution of navigation equations, it is important to know about the reference frames and the transformation of coordinates between them. But first we will review some of the basic mathematical techniques.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 3. Global Positioning System
Abstract
The global positioning system (GPS) was developed by the US Department of Defense in the early 1970s to serve military navigational requirements. The first satellite was launched in 1978 and the system was declared operational in 1995. It is based on a network of at least 24 satellites (with room for six further satellites) orbiting the Earth in nearly circular orbits with a mean radius of about 26,560 km.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 4. Inertial Navigation System
Abstract
An inertial navigation system is an autonomous system that provides information about position, velocity and attitude based on the measurements by inertial sensors and applying the dead reckoning (DR) principle. DR is the determination of the vehicle’s current position from knowledge of its previous position and the sensors measuring accelerations and angular rotations. Given specified initial conditions, one integration of acceleration provides velocity and a second integration gives position. Angular rates are processed to give the attitude of the moving platform in terms of pitch, roll and yaw, and also to transform navigation parameters from the body frame to the local-level frame.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 5. Inertial Navigation System Modeling
Abstract
Modeling requires representing real world phenomena by mathematical language. To keep the problem tractable the goal is not to produce the most comprehensive descriptive model but to produce the simplest possible model which incorporates the major features of the phenomena of interest. The model is also restricted by the ability of mathematics to describe a phenomenon. This book deals with models which describe the motion of an object on or near the surface of the Earth. This kind of motion is greatly influenced by the geometry of the Earth. There are two broad categories for modeling motion: dynamic and kinematic.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 6. Modeling INS Errors by Linear State Equations
Abstract
The accuracy of an INS is affected by various sources. These include errors during the initial alignment procedure, sensor errors, and the limitations of the processing algorithm. To see the effect of these errors on the navigational output parameters (position, velocity and attitude) it is vital to understand their propagation through the navigation equations. Once the nature of the errors is known, one can mitigate them by proper modeling and estimation techniques. This usually requires external aiding sources in order to limit the errors and predict their behavior. Hence error models are required for the analysis and estimation of the error sources associated with any inertial navigation system.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 7. Kalman Filter
Abstract
As stated in the previous chapter the accuracy of an INS is affected by the errors in the inertial sensors, initialization and computational algorithms. The situation is worse for the low cost MEMS sensors where the INS output can drift rapidly and render them essentially unusable as standalone sensors for navigation applications owing to severe stochastic errors.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 8. INS/GPS Integration
Abstract
There are contrasting pros and cons to INS and GPS. An INS is a self-contained autonomous navigation system that provides a bandwidth exceeding 200 Hz. It has good short term accuracy and provides attitude information in addition to position and velocity.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 9. Three-Dimensional Reduced Inertial Sensor System/GPS Integration for Land-Based Vehicles
Abstract
This chapter discusses a dead reckoning (DR) solution which is suitable for any wheel-based platform integrated with GPS. It eliminates several error sources that exist when using a traditional full IMU, especially low cost MEMS grade sensors. After discussing and analyzing the performance of a full IMU system, the theory of the methods employed to tackle sources of errors will be outlined. The reduced inertial sensor system is introduced and compared to a traditional full IMU, and its mechanization equations derived. This chapter discusses a dead reckoning (DR) solution which is suitable for any wheel-based platform integrated with GPS. It eliminates several error sources that exist when using a traditional full IMU, especially low cost MEMS grade sensors. After discussing and analyzing the performance of a full IMU system, the theory of the methods employed to tackle sources of errors will be outlined. The reduced inertial sensor system is introduced and compared to a traditional full IMU, and its mechanization equations derived. This is followed by a description of both loosely and tightly coupled KF-based integration of this reduced inertial sensor system with GPS, including the linearized system model and measurement model for each integration scheme.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Chapter 10. Two Case Studies: Full IMU/GPS and 3D RISS/GPS Integration
Abstract
In Chap. 8 the theories of the loosely coupled and tightly coupled integration schemes of a full IMU with GPS were presented. In Chap. 9 the integration of a reduced inertial sensor system (RISS) with GPS was detailed, along with its various advantages. In this chapter we will look at the performance of these integration techniques using real inertial measurements and GPS data collected during road test trajectories.
Aboelmagd Noureldin, Tashfeen B. Karamat, Jacques Georgy
Metadaten
Titel
Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration
verfasst von
Aboelmagd Noureldin
Tashfeen B. Karamat
Jacques Georgy
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-30466-8
Print ISBN
978-3-642-30465-1
DOI
https://doi.org/10.1007/978-3-642-30466-8

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