TY - JOUR
A2 - Liu, Ruihua
AU - Pardal, Paula Cristiane Pinto Mesquita
AU - Kuga, Helio Koiti
AU - de Moraes, Rodolpho Vilhena
PY - 2015
DA - 2015/10/18
TI - The Particle Filter Sample Impoverishment Problem in the Orbit
Determination Application
SP - 168045
VL - 2015
AB - 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.
SN - 1024-123X
UR - https://doi.org/10.1155/2015/168045
DO - 10.1155/2015/168045
JF - Mathematical Problems in Engineering
PB - Hindawi Publishing Corporation
KW -
ER -