@Article{PardalKugaMora:2015:PaFiSa,
               author = "Pardal, Paula Cristiane Pinto Mesquita and Kuga, H{\'e}lio Koiti 
                         and Moraes, Rodolpho Vilhena de",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal de S{\~a}o 
                         Paulo (UNIFESP)}",
                title = "The particle filter sample impoverishment problem in the orbit 
                         determination application",
              journal = "Mathematical Problems in Engineering",
                 year = "2015",
               volume = "2015",
                pages = "168045",
             abstract = "The paper aims at discussing techniques for administering one 
                         implementation issue that often arises in the application of 
                         particle filters: sample impoverishment. Dealing with such problem 
                         can significantly improve the performance of particle filters and 
                         can make the difference between success and failure. Sample 
                         impoverishment occurs because of the reduction in the number of 
                         truly distinct sample values. A simple solution can be to increase 
                         the number of particles, which can quickly lead to unreasonable 
                         computational demands, which only delays the inevitable sample 
                         impoverishment. There are more intelligent ways of dealing with 
                         this problem, such as roughening and prior editing, procedures to 
                         be discussed herein. The nonlinear particle filter is based on the 
                         bootstrap filter for implementing recursive Bayesian filters. The 
                         application consists of determining the orbit of an artificial 
                         satellite using real data from the GPS receivers. The standard 
                         differential equations describing the orbital motion and the GPS 
                         measurements equations are adapted for the nonlinear particle 
                         filter, so that the bootstrap algorithm is also used for 
                         estimating the orbital state. The evaluation will be done through 
                         convergence speed and computational implementation complexity, 
                         comparing the bootstrap algorithm results obtained for each 
                         technique that deals with sample impoverishment.",
                  doi = "10.1155/2015/168045",
                  url = "http://dx.doi.org/10.1155/2015/168045",
                 issn = "1024-123X",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}