@InProceedings{PardalKugaMora:2010:CoExSi,
               author = "Pardal, P. C. P. M. and Kuga, Helio Koiti and Moraes, R. Vilhena 
                         de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Comparing the extended and sigma point Kalman filters for orbit 
                         determination modeling using GPS measurements",
            booktitle = "Proceedings...",
                 year = "2010",
         organization = "23rd International Technical Meeting of the Satellite Division of 
                         ION – ION GNSS",
             abstract = "The purpose of this work is to compare the extended Kalman filter 
                         (EKF) against the nonlinear sigma point Kalman filter (SPKF) for 
                         the satellite orbit determination problem, using GPS measurements. 
                         The comparison is based on the levels of accuracy improvement of 
                         the orbit dynamics model. The main subjects for the comparison 
                         between the estimators are accuracy of models and results. Based 
                         on the analysis of such criteria, the advantages and drawbacks of 
                         each estimator are presented. In this work, the orbit of an 
                         artificial satellite is determined using real data from the Global 
                         Positioning System (GPS) receivers. In orbit determination of 
                         artificial satellites, the dynamic system and the measurements 
                         equations are of nonlinear nature. It is a nonlinear problem in 
                         which the disturbing forces are not easily modeled. The problem of 
                         orbit determination consists essentially of estimating parameter 
                         values that completely specify the body trajectory in the space, 
                         processing a set of information (measurements) related to this 
                         body. Such observations can be collected through a ground tracking 
                         network on Earth or through sensors, like space GPS receivers 
                         onboard the satellite. The EKF implementation in orbit estimation, 
                         under inaccurate initial conditions and scattered measurements, 
                         can lead to unstable or diverging solutions. For solving the 
                         problem of nonlinear nature, convenient extensions of the Kalman 
                         filter have been sought. In particular, the unscented 
                         transformation was developed as a method to propagate mean and 
                         covariance information through nonlinear transformations. The 
                         Sigma Point Kalman Filter (SPKF) appears as an emerging estimation 
                         algorithm applied to nonlinear systems, without needing 
                         linearization steps. In this orbit determination case study the 
                         focus is to gradually improve the dynamical model, which presents 
                         highly nonlinear properties, and to know how it affects the 
                         performance of the estimators. Therefore, the EKF (the most widely 
                         used real time estimation algorithm) as well as the SPKF 
                         (supposedly one of the most appropriate estimation algorithm for 
                         nonlinear systems) performance evaluation is justified. The aim of 
                         this work is to analyze the new nonlinear estimation technique, 
                         the SPKF, in an actual orbit determination problem with actual 
                         measurements data from GPS, and to compare it with a widely used 
                         technique, the EKF, pinpointing the main differences between both 
                         the algorithms.",
  conference-location = "Portland, Oregon",
      conference-year = "21-24 sep. 2010",
             language = "en",
           targetfile = "pardal_comparing.pdf",
        urlaccessdate = "21 maio 2024"
}