Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles

Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles

Application to Guidance and Navigation of Unmanned Aerial Vehicles Flying in a Complex Environment

1st Edition - November 8, 2018

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  • Author: Jean-Philippe Condomines
  • eBook ISBN: 9780081027448
  • Hardcover ISBN: 9781785482854

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Description

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.

Key Features

  • 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

Readership

Scientists, researchers and engineers interested in this subject area

Table of Contents

  • 1. Introduction to Aerial Robotics
    2. The State of the Art
    3. Inertial Navigation Models
    4. The IUKF and π-IUKF Algorithms
    5. Methodological Validation, Experiments and Results

Product details

  • No. of pages: 254
  • Language: English
  • Copyright: © ISTE Press - Elsevier 2018
  • Published: November 8, 2018
  • Imprint: ISTE Press - Elsevier
  • eBook ISBN: 9780081027448
  • Hardcover ISBN: 9781785482854

About the Author

Jean-Philippe Condomines

Jean-Philippe Condomines is Assistant Professor in Guidance Navigation and Control in the UAV team at the French National Civil Aviation University (ENAC), in Toulouse, France, where he contributes to the development of an open source pilot for the Paparazzi project. He received in 2015 his Ph.D. in Automatic Control from the Higher Institute of Aeronautics and Space (ISAE), in Toulouse, France. Incompared by a nonlinear state estimation, named Invariant Unscented Kalman Filter (IUKF), based on both nonlinear invariant observers and UKF. UAV (Gust Energy Extraction for Mini- and Micro-UAV, Non-linear control design for in-flight Loss-of-control, Adaptative control design for fixed-wing and security issues in UAVs Ad - hoc networks (IDS) for aeronautics, Ad hoc network Dynamic modeling, IDS using robust controller / observer, Applications de invariant methodology à classification des air traffic density.

Affiliations and Expertise

Assistant Professor in Guidance Navigation and Control, UAV Team, Fench National Civil Aviation University (ENAC), Toulouse, France

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