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Sensor Fusion Using Kalman Filter for a Quadrotor-Attitude Estimation: Basics, Concepts, Modelling, Matlab Code and Experimental Validation - Couverture souple

Ayman, Samir

 
9783659854682: Sensor Fusion Using Kalman Filter for a Quadrotor-Attitude Estimation: Basics, Concepts, Modelling, Matlab Code and Experimental Validation
Couverture souple
ISBN 10 : 3659854689 ISBN 13 : 9783659854682
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

Quad rotors are types of unmanned air vehicles that became an attractive topic to an enormous number of people worldwide. This book focuses on sensor fusion of low-cost inertial sensors: a tri-axis accelerometer, a tri-axis gyroscope and a tri-axis magnetometer. Three types of filters were implemented in this book: two models of linear complementary filter (LCF), two models of linear Kalman filter (KF) and a quaternion-based extended Kalman filter (EKF). A total of five filter models were tested and verified using experimental data sets through three distinct scenarios. Six different data sets were chosen, each with antithetic characteristics, to test the filters on them. On the basis of the results of this research, it can be concluded that taking into consideration accelerometer bias improves the performance of the filter. KF shows the best performance as it combines the characteristics of short computing time and accurate estimation results suiting the aim of the book which is to allow the quad rotor to hover.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Quad rotors are types of unmanned air vehicles that became an attractive topic to an enormous number of people worldwide. This book focuses on sensor fusion of low-cost inertial sensors: a tri-axis accelerometer, a tri-axis gyroscope and a tri-axis magnetometer. Three types of filters were implemented in this book: two models of linear complementary filter (LCF), two models of linear Kalman filter (KF) and a quaternion-based extended Kalman filter (EKF). A total of five filter models were tested and verified using experimental data sets through three distinct scenarios. Six different data sets were chosen, each with antithetic characteristics, to test the filters on them. On the basis of the results of this research, it can be concluded that taking into consideration accelerometer bias improves the performance of the filter. KF shows the best performance as it combines the characteristics of short computing time and accurate estimation results suiting the aim of the book which is to allow the quad rotor to hover.

Biographie de l'auteur

Samir Ayman Abdel Aal, born in Cairo on the 2nd of March 1993. Graduated with a BSc. in Mechatronics Engineering at the German University in Cairo in July 2015. Currently working as an Account Manager at IBM, Cairo, Egypt.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
3659854689
ISBN 13 
9783659854682
Reliure
Broché
Nombre de pages
200
Coordonnées du fabricant
non disponible
Personne responsable
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