@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 = "21 maio 2024"
}