@InProceedings{GarciaPardKuga:2018:CuKaFi,
author = "Garcia, Roberta Veloso and Pardal, C. Paula M. and Kuga,
H{\'e}lio Koiti",
affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Universidade de
S{\~a}o Paulo (USP)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Nonlinear filtering for sequential spacecraft attitude estimation
with real data: cubature kalman filter, unscented kalman filter
and extended kalman filter",
year = "2018",
organization = "Cospar Scientific Assembly, 42.",
abstract = "The purpose of this work is to analyze the performance of the
Cubature Kalman Filter, Unscented Kalman Filter and Extended
Kalman Filter estimators in the attitude estimation problem when
submitted to real attitude sensors data. The Extended Kalman
Filter (EKF) is the most used nonlinear filtering algorithm for
the attitude estimation in real time. The EKF is the nonlinear
version of the Kalman Filter which linearizes about an estimate of
the current mean and covariance. However, when the filter is
subjected to poor conditions, the linearization of the system may
not be efficient and lead to an estimation of low accuracy and
divergence of the filter. The Unscented Kalman Filter (UKF) is an
algorithm that was developed in order to avoid the linearizations
required by the EKF. Basically, the UKF uses a set of points
chosen deterministically, called sigma-points, to capture the
probability distribution and generalizes to nonlinear system
without the burdensome analytic derivation as in the EKF. More
recently, the Cubature Kalman Filter (CKF) was proposed as an
alternative estimation algorithm for general nonlinear systems.
The CKF, which builds on the numerical-integration perspective of
Gaussian filters, employs a third-degree spherical-radical
cubature rule to compute Gaussianweighted integrals,
derivative-free nonlinear filtering algorithm with improved
performance over the UKF in terms of estimation accuracy,
numerical stability and computational costs. In this work, the
application uses the real measurement data for orbit and attitude
of the CBERS-2 (China Brazil Earth Resources Satellite) satellite.
The attitude dynamical model is described by nonlinear equations
involving the Euler angles. The attitude sensors available are two
DSS (Digital Sun Sensors), two IRES (Infra-Red Earth Sensor), and
one triad of mechanical gyros. The analyzes are based on the
robustness of the filter, in relation to the precision,
computational cost and convergence speed in attitude estimation.
As the use of real data makes it impossible to compare the
estimated results with the real attitude of the satellite, then
the results obtained via EKF are taken as reference for comparison
with the UKF and CKF. The results in this work show that, for the
case studied in this article, the filters are very competitive and
present advantages and disadvantages that should be evaluated
according to the need of each problem.",
conference-location = "Pasadena, California",
conference-year = "14-22 July",
language = "en",
targetfile = "garcia_nonlinear.pdf",
urlaccessdate = "27 abr. 2024"
}