@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"
}