@InCollection{PenhaNetoCampShig:2019:ImPrUA,
               author = "Penha Neto, Gerson da and Campos Velho, Haroldo Fraga de and 
                         Shiguemori, Elcio Hideki",
                title = "Image processing for UAV autonomous navigation applying 
                         self-configuring neural network",
            booktitle = "Integral methods in science and engineering",
            publisher = "Springer",
                 year = "2019",
               editor = "Constanda, Christian and Harris, Paul",
                pages = "321--342",
              address = "Brighton, UK",
             keywords = "Unmanned aerial vehicles, Kalman filter, artificial neural 
                         networks.",
             abstract = "Application and development of Unmanned Aerial Vehicles (UAV) have 
                         had a rapid growth. The flight control of these aircarfts can be 
                         performed remotely or autonomously. There are different strategies 
                         for the UAV autonomous navigation. The positioning estimation can 
                         be done by using inertial sensors and General Navigation Satellite 
                         Systems (GNSS). The use of the GNSS signal can present some 
                         difficulties: natural or not natural interference. An alternative 
                         for positioning adjustment is to use a data fusion from different 
                         sensors by a Kalman filter. A supervised artificial network (ANN) 
                         is trained to emulate the filter for reducing the computational 
                         effort. An automatic best topology for the neural network is 
                         obtained by minimizing a functional by a new meta-heurisc called 
                         Multi-Particle Collision Algorithm (MPCA). Our results show 
                         similar accuracy between the ANN and the Kalman filter, with 
                         better processing performance to the neural network.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                 isbn = "978-3-030-16077-7",
             language = "en",
           targetfile = "Penha_image.pdf",
        urlaccessdate = "27 abr. 2024"
}