@Article{PenhaNetoCampShig:2019:UAAuNa,
               author = "Penha Neto, Gerson da and Campos Velho, Haroldo Fraga de and 
                         Shiguemori, Elcio Hieiti",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "UAV autonomous navigation by data fusion and FPGA",
              journal = "Mec{\'a}nica Computacional",
                 year = "2019",
               volume = "37",
               number = "16",
                pages = "609--618",
                month = "05-07 nov.",
             keywords = "Unmanned aerial vehicles, FPGA: Field Programmable Gate Array, 
                         autonomous navigation, self-configuring neural network.",
             abstract = "Currently, the use of unmanned aerial vehicles (UAV), also known 
                         as drones, is increasing. The applications are in several areas 
                         such as engineering projects, agriculture, livestock, monitoring, 
                         and rescue. One of the main reasons to use UAV is its lower cost 
                         when compared to manned aircraft. The flight of a UAV can be done 
                         remotely or autonomously. For the autonomous navigation, a Global 
                         Navigation Satellite System (GNSS) is usually applied. However, a 
                         GNSS system can suffer natural or human interference, becoming the 
                         research for alternatives strategies a hot topic in this field. An 
                         approach to carry out the autonomous navigation without use of 
                         GNSS signal is to estimate the UAV position by using data fusion 
                         combining different sensors. A solution for autonomous navigation 
                         is presented applying inertial sensor and image processing, both 
                         are employed to estimate the drone position. The data fusion 
                         process is carried out by a computational intelligence procedure. 
                         Two self-configuring ANNs are employed here: for image edge 
                         extraction, and an operator for data fusion. A hybrid computer 
                         architecture is employed to implement the solution with standard 
                         CPU and FPGA (Field Programmable Gate Array).",
             language = "en",
           targetfile = "penha_uav.pdf",
        urlaccessdate = "27 abr. 2024"
}