author = "Arnas, David and Fialho, M{\'a}rcio Afonso Arimura and Mortari, 
          affiliation = "{Centro Universitario de la Defensa Zaragoza} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Texas A \& M 
                title = "Robust triad and quad generation algorithms for star trackers",
              journal = "Advances in the Astronautical Sciences",
                 year = "2017",
               volume = "160",
                pages = "859--878",
                 note = "27th AAS/AIAA Space Flight Mechanics Meeting, 2017; San Antonio; 
                         United States; 05-09 Feb. 2017.",
             abstract = "Star Identification (Star-ID) is a complex problem, mainly because 
                         some of the observations are not generated by actual stars, but by 
                         reflecting debris, other satellites, visible planets, or by 
                         electronic noise. For this reason, the capability to discriminate 
                         stars from non-stars is an important aspect of Star-ID robustness. 
                         Usually, the Star-ID task is performed by first attempting 
                         identification on a small group of observed stars (a kernel) and, 
                         in case of failure, replacing that kernel with another until a 
                         kernel made only of actual stars is found. This work performs a 
                         detailed analysis of kernel generator algorithms, suitable for 
                         onboard implementation in terms of speed and robustness, for 
                         kernels of three (triad) and four (quad) stars. Three new kernel 
                         generator algorithms and, in addition to the existing expected 
                         time to discovery, three new metrics for robustness evaluation are 
                         proposed. The proposed algorithms are fast, robust to find good 
                         kernels, and do not require pre-stored data.",
                 issn = "0065-3438.",
             language = "en",
           targetfile = "arnas_robust.pdf",
        urlaccessdate = "24 nov. 2020"