Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00302461" target="_blank" >RIV/68407700:21230/16:00302461 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.intechopen.com/books/recent-advances-in-robotic-systems/validation-and-experimental-testing-of-observers-for-robust-gnss-aided-inertial-navigation" target="_blank" >http://www.intechopen.com/books/recent-advances-in-robotic-systems/validation-and-experimental-testing-of-observers-for-robust-gnss-aided-inertial-navigation</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5772/63575" target="_blank" >10.5772/63575</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

Popis výsledku v původním jazyce

This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements.

Název v anglickém jazyce

Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

Popis výsledku anglicky

This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements.

Druh

C - Kapitola v odborné knize

CEP obor

JW - Navigace, spojení, detekce a protiopatření

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Recent Advances in Robotic Systems

ISBN

978-953-51-2570-9

Počet stran výsledku

284

Strana od-do

1-284

Počet stran knihy

292

Název nakladatele

InTech - Open Access Company (InTech Europe)

Místo vydání

Rijeka

Kód UT WoS kapitoly

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