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@InProceedings{PardalMoraKuga:2014:OrDeUs,
               author = "Pardal, P. C. P. M. and Moraes, R. V. and Kuga, H{\'e}lio Koiti",
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Orbit determination using nonlinear particle filter and GPS 
                         measurements",
            booktitle = "Proceedings...",
                 year = "2014",
                pages = "1077--1092",
         organization = "AAS/AIAA Astrodynamics Specialist Conference.",
            publisher = "Univelt Inc.",
              address = "Hilton Head Island",
             abstract = "A particle filter, specifically a Bayesian bootstrap filter 
                         algorithm, is applied for estimating the state vector that 
                         characterizes the orbit of a satellite, using a set of GPS 
                         measurements. The development will be evaluated through 
                         performance and computational cost, comparing the bootstrap 
                         algorithm results against the unscented Kalman filter (UKF) 
                         solution for the same problem. The orbit determination is a 
                         nonlinear problem, with respect to the dynamics and the 
                         measurements equations, in which the disturbing forces and the 
                         measurements are not easily modeled. It consists essentially of 
                         estimating values that completely specify the body trajectory in 
                         the space, processing a set of measurements related to this body. 
                         Such observations can be collected through a tracking network 
                         grounded on Earth or through sensors, like GPS receivers onboard 
                         the satellite. The GPS is a wide spread system that allows 
                         computation of orbits for artificial Earth satellites by providing 
                         many redundant measurements (pseudo-ranges). The bootstrap filter 
                         is proposed for implementing recursive Bayesian filters. It is a 
                         statistical, brute-force approach to estimation that often works 
                         well for systems that are highly nonlinear. Here, the bootstrap 
                         particle filter will be implemented with resampling and 
                         roughening, a scheme for combating the reduction in the number of 
                         truly distinct sample values.",
  conference-location = "South Carolina",
      conference-year = "11-15 Aug.",
                 isbn = "9780877036050",
                 issn = "0065-3438",
                label = "scopus 2014-05 PardalMoraKuga:2014:OrDeUs",
             language = "en",
               volume = "150",
        urlaccessdate = "28 mar. 2024"
}


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