@Article{PenhaNetoCampShig:2023:DaSeTr,
               author = "Penha Neto, Gerson da and Campos Velho, Haroldo Fraga de and 
                         Shiguemori, Elcio Hideiti",
          affiliation = "{Faculdade de Tecnologia (FATEC)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto de Estudos Avanc¸ados 
                         (IEAv)}",
                title = "Data Selection for Training the Neural Fuser Applied to Autonomous 
                         UAV Navigation",
              journal = "Trends in Computational and Applied Mathematics",
                 year = "2023",
               volume = "24",
               number = "1",
                pages = "159--175",
             keywords = "Self-configured neural network, Unmanned aerial vehicle (UAV), 
                         Cross-validation, k-fold.",
             abstract = "Over the past few years, there has been a steady increase in the 
                         use of aircraft vehicles, in particular unmanned aerial vehicles 
                         (UAV). UAV navigation is generally controlled by a human pilot. 
                         But the challenge for the scientific community is to carry out 
                         autonomous navigation. Some solutions have been proposed for the 
                         UAV autonomous navigation. Studies indicate as a solution to use 
                         data fusion and/or image processing navigation. Kalman Filter (KF) 
                         can be employed as a data fuser, but the KF has disadvantages. An 
                         alternative to the KF is based on artificial intelligence. Here, 
                         the KF is replaced by a self-configured neural network. This work 
                         investigates a way to select data for training the neural fuser, 
                         based on crossvalidation techniques. The results are compared to 
                         the data fusion done by a KF.",
                  doi = "10.5540/tcam.2022.024.01.00159",
                  url = "http://dx.doi.org/10.5540/tcam.2022.024.01.00159",
                 issn = "2676-0029",
                label = "lattes: 5142426481528206 2 PenhaNetoCampShig:2023:DaSeTr",
             language = "pt",
           targetfile = "MzcHRCYbQHYGM6M7tzNFMnF.pdf",
                  url = "https://tema.sbmac.org.br/tema",
        urlaccessdate = "12 maio 2024"
}