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

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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

Jean-Philippe Condomines

Iste Press - Elsevier
Publication date : 2018-11-14

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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

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About the book

Author

Jean-Philippe Condomines

Imprint

Iste Press - Elsevier

Collection

n.c

Publication date

2018-11-14

Pages

254 pages

Print ISBN

9781785482854

Language

English

Ebook informations

EAN EPUB DRM-FREE

9780081027448

Price

£89.62

Informations accessibility

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About author(s)

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.

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