Application to guidance and navigation of unmanned aerial vehicles flying in a complex environment / Jean-Philippe Condomines.

Format:

Book

Author/Creator:

Condomines, Jean-Philippe, author.

Series:

Nonlinear Kalman filtering for multi-sensor navigation of unmanned aerial vehicles

Language:

English

Subjects (All):

Drone aircraft--Automatic control.

Drone aircraft.

Guidance systems (Flight).

Drone aircraft--Piloting--Mathematics.

Drone aircraft--Piloting--Planning.

Airways--Mathematical models.

Airways.

Physical Description:

1 online resource (256 pages) : illustrations (some color).

Place of Publication:

London : Elsevier, 2018.

Summary:

Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles covers state estimation development approaches for Mini-UAV. The book focuses on Kalman filtering technics for UAV design, proposing a new design methodology and case study related to inertial navigation systems for drones. Both simulation and real experiment results are presented, thus showing new and promising perspectives.- Gives a state estimation development approach for mini-UAVs- Explains Kalman filtering techniques- Introduce a new design method for unmanned aerial vehicles- Introduce cases relating to the inertial navigation system of drones

Contents:

Front Cover

Nonlinear Kalman Filtering for Multi-Sensor Navigation of Unmanned Aerial Vehicles: Application to Guidance and Navigation of Unmanned Aerial Vehicles Flying in a Complex Environment

Copyright Page

Contents

Preface

Organization of this book

1. Introduction to Aerial Robotics

1.1. Aerial robotics

1.2. The Paparazzi project

1.3. Measurement techniques

1.4. Motivation

2. The State of the Art

2.1. Basic concepts

2.2. Literature review

2.3. Optimal filtering with linear system models

2.4. Approximating the optimal filter by linearization: the EKF

2.5. Approximating the optimal filter by discretization: the Sigma Points Kalman Filter

2.6. Invariant observer theory

3. Inertial Navigation Models

3.1. Preliminary remarks: modeling mini-UAVs

3.2. Derivation of the navigation model

3.3. The problem of "true" inertial navigation

3.4. Modeling and identifying the imperfections of inertial sensors

3.5. Inertial navigation on low budgets: AHRS

3.6. AHRS plus a GPS and a barometer: Inertial Navigation System

4. The IUKF and π-IUKF Algorithms

4.1. Preliminary remarks

4.2. Organization of this chapter

4.3. Results from differential geometry: symmetries and invariant/equivariant systems

4.4. Invariant observers - AHRS/INS

4.5. Invariant state estimation error

4.6. The SR-UKF algorithm

4.7. First reformulation of unscented Kalman filtering in an invariant setting: the IUKF algorithm

4.8. Second reformulation of unscented Kalman filtering in an invariant setting: the π-IUKF algorithm

5. Methodological Validation, Experiments and Results

5.1. Validation with simulated data

5.2. Validation with real data

Conclusion and Outlook

Appendix: Differential Geometry and Group Theory

References

Index

Back Cover.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed December 3, 2018).

ISBN:

9780081027448

0081027443