@InProceedings{PardalKugaGarc:2018:InCuKa,
               author = "Pardal, Paula C. P. M. and Kuga, H{\'e}lio Koiti and Garcia, 
                         Roberta Veloso",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade de S{\~a}o Paulo 
                         (USP)}",
                title = "An investigation into cubature kalman filter performance for orbit 
                         determination application",
                 year = "2018",
         organization = "Cospar Scientific Assembly, 42.",
             abstract = "The purpose of this work is to discuss the Cubature Kalman Filter 
                         (CKF) performance when applied to a high nonlinear problem: 
                         artificial satellites orbit determination, using real Global 
                         Positioning System (GPS) data. The CKF is a discrete-time 
                         nonlinear Bayesian filter based on a third-degree spherical-radial 
                         cubature rule, which allows to numerically computing multivariate 
                         moment integrals in the Bayesian filter. In particular, it also 
                         provides a set of cubature points scaling linearly with the state 
                         vector dimension. As a result, the CKF yields a systematic 
                         solution for high dimensional nonlinear filtering problems, such 
                         as the orbit determination addressed here. In this work, the 
                         application consists of determining the orbit of an artificial 
                         satellite, using real data from the GPS receivers. This is a 
                         nonlinear problem, with respect to the dynamics and measurements 
                         equations, in which the disturbing forces are not easily modeled. 
                         The problem of orbit determination consists essentially of 
                         estimating values that completely specify the body trajectory in 
                         the space, processing a set of observations that can be collected 
                         through a tracking network grounded on Earth or through sensors, 
                         like space GPS receivers onboard the satellite. The GPS is a 
                         widespread system that allows computation of orbits for artificial 
                         Earth satellites by providing many redundant measurements. 
                         Throughout an onboard GPS receiver, it is possible to obtain 
                         nonlinear measurements (pseudoranges) that can be processed to 
                         estimate the orbital state. The standard differential equations 
                         describing the orbital motion and the GPS measurements equations 
                         are adapted for the nonlinear filter so that the CKF algorithm is 
                         also used for estimating the orbital state. The assessment to be 
                         presented will be based on the robustness of the filter, 
                         concerning convergence speed when the measurements are scattered. 
                         The results from CKF will be compared to the unscented Kalman 
                         filter (UKF) results for the same problem, in computational terms 
                         such as convergence and accuracy. Based on the analysis of such 
                         criteria, the advantages and drawbacks of the implementations are 
                         presented.",
  conference-location = "Pasadena, California",
      conference-year = "14-22 July",
             language = "en",
           targetfile = "pardal_investigation.pdf",
        urlaccessdate = "27 abr. 2024"
}