Sensor Fusion Using Kalman Filter for a Quadrotor-Attitude Estimation (Paperback)

Samir Ayman

ISBN 10: 6209774792 / ISBN 13: 9786209774799

Published by LAP Lambert Academic Publishing, 2026

About this Item

Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9786209774799

Bibliographic Details

• Title: Sensor Fusion Using Kalman Filter for a Quadrotor-Attitude Estimation (Paperback)
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6209774792
• ISBN 13: 9786209774799
• Binding: Paperback
• Condition: new

About this title

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.