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Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition

Paul D Groves
TLDR
The second edition of the Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems as discussed by the authors offers a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching.
Abstract
This newly revised and greatly expanded edition of the popular Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems offers you a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching . It provides both an introduction to navigation systems and an in-depth treatment of INS/GNSS and multisensor integration. The second edition offers a wealth of added and updated material, including a brand new chapter on the principles of radio positioning and a chapter devoted to important applications in the field. Other updates include expanded treatments of map matching, image-based navigation, attitude determination, acoustic positioning, pedestrian navigation, advanced GNSS techniques, and several terrestrial and short-range radio positioning technologies. The book shows you how satellite, inertial, and other navigation technologies work, and focuses on processing chains and error sources. In addition, you get a clear introduction to coordinate frames, multi-frame kinematics, Earth models, gravity, Kalman filtering, and nonlinear filtering. Providing solutions to common integration problems, the book describes and compares different integration architectures, and explains how to model different error sources. You get a broad and penetrating overview of current technology and are brought up to speed with the latest developments in the field, including context-dependent and cooperative positioning. DVD Included: Features eleven appendices, interactive worked examples, basic GNSS and INS MATLAB simulation software, and problems and exercises to help you master the material.

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Citations
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Indoor Tracking: Theory, Methods, and Technologies

TL;DR: A survey on indoor wireless tracking of mobile nodes from a signal processing perspective and it can be argued that the indoor tracking problem is more challenging than the problem on indoor localization.
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Fiber ring interferometer

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A Theoretical Foundation of Network Localization and Navigation

TL;DR: A theoretical foundation of NLN is presented, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy.
Journal ArticleDOI

Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment

TL;DR: An optimization-based coarse alignment approach that uses GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae is proposed, and can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment.
Journal ArticleDOI

Height Aiding, C/N 0 Weighting and Consistency Checking for GNSS NLOS and Multipath Mitigation in Urban Areas

TL;DR: Three different techniques for mitigating the impact of non-line-of-sight (NLOS) reception and multipath interference on position accuracy without using additional hardware are investigated, testing them using data collected at multiple sites in central London.
References
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Book ChapterDOI

A New Approach to Linear Filtering and Prediction Problems

TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Journal ArticleDOI

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Proceedings ArticleDOI

New extension of the Kalman filter to nonlinear systems

TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.