+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)

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

  • Book
  • November 2018
  • Elsevier Science and Technology
  • ID: 4579889

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.

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Table of Contents

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

Authors

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