Название: 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 Автор: Jean-Philippe Condomines
Издательство: ISTE Press - Elsevier
Год: 2018
Язык: английский
Формат: True PDF
Размер: 26.0 MB
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.
The use of Unmanned Aerial Vehicles (UAVs) is exploding in the civil sector. Although UAVs are old news in the military sector, they are a brand new field for civil applications, such as pipeline monitoring, public protection or tools for processing and analyzing crops. New applications that use UAVs as experimental vectors are currently being researched. Among many other possible applications, UAVs seem especially promising in the fields of aerology and meteorology, where they can be used to study and measure local phenomena such as wind gradients and cloud formations. Interestingly, since 2006, mini-UAVs and micro-UAVs account for most new aircraft. Both belong to the category of sub-30-kg UAVs, which will be the primary focus of this book. These aircraft have the advantage of being relatively lightweight and easy to transport, unlike other types of UAV, which can weigh over 150 kg. Other than weight, UAVs can be classified by battery life, which determines their operating range. Accordingly, they are often categorized as Short Range (SR), Close Range (CR) or Medium Range (MR) aircraft.
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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