@InProceedings{SalazarCarv:2014:StTrOr,
               author = "Salazar, Francisco Javier Tipan and Carvalho, Fabr{\'{\i}}cio 
                         Galende Marques de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Star Tracker Orientation Optimization Using Non-Dominated Sorting 
                         Genetic Algorithm (NSGA)",
            booktitle = "Proceedings...",
                 year = "2014",
                pages = "1--8",
         organization = "IEEE Aerospace Conference.",
             keywords = "Non-Dominated Sorting Genetic Algorithm.",
             abstract = "One of the devices used to determine the attitude of a satellite 
                         is the star tracker, whose principle of operation is based on star 
                         position measurements on a specific inertial frame, allowing 
                         precise attitude determination and control of the satellite. Due 
                         to the high sensitivity of star camera, bright objects like Sun, 
                         Earth or Moon must be avoided in the sensors field of view. This 
                         characteristic imposes a design constraint that shall be satisfied 
                         simultaneously by all the star trackers used in the specific 
                         satellite. Considering only the Sun exclusion case, the goal of 
                         this work is to find the star tracker orientation that maximizes 
                         simultaneously the Sun exclusion angle for both sensors in a way 
                         to ensure the proper equipment operation during a typical Earth 
                         pointing satellite mission. For this optimization problem, the 
                         search space is defined by the azimuth and elevation of each star 
                         tracker in the body centered coordinate frame system. Since the 
                         engineering goal implies a vectorial objective function 
                         optimization, the method used in this work was the Non-dominated 
                         Sorting Genetic Algorithm (NSGA), which allows a 
                         multi-optimization problem solution without the scalarization 
                         approach, in order to get a few optimal solutions along the 
                         non-dominated region. In order to get diversity in the optimal 
                         solutions, simulations used six different dummy fitness functions 
                         and compared the final results.",
  conference-location = "Big Sky, EUA",
      conference-year = "mar. 1, 2014.",
                label = "self-archiving-INPE-MCTI-GOV-BR",
             language = "en",
           targetfile = "06836461.pdf",
        urlaccessdate = "26 abr. 2024"
}