Unscented Kalman Filter for Determination of Spacecraft Attitude Using Different Attitude Parameterizations and Real Data

AUTOR(ES)

Garcia, Roberta Veloso, Kuga, Hélio Koiti, Zanardi, Maria Cecília F. P. S.

FONTE

J. Aerosp. Technol. Manag.

DATA DE PUBLICAÇÃO

2016-03

RESUMO

ABSTRACT The non-linear estimators are certainly the most important algorithms applied to real problems, especially those involving the attitude estimation of spacecraft. The purpose of this paper was to use real data of sensors to analyze the behavior of Unscented Kalman Filter (UKF) in attitude estimation problems when it is represented in different ways and compare it with the standard estimator for non-linear estimation problems. The robustness of the estimation was performed when this was subjected to imprecise initial conditions. The attitude parametrization was described in Euler angles, quaternion and quaternion incremental. The satellite China-Brazil Earth Resources Satellite and measurements provided by the Satellite Control Center of the Instituto Nacional de Pesquisas Espaciais were considered in the study. The results indicate that the behaviors for both estimators were equivalent for such parameterizations under the same conditions. However, comparing the Unscented Kalman Filter with the standard filter for non-linear systems, Extended Kalman Filter (EKF), it was observed that, in the presence of inaccurate initial conditions, the Unscented Kalman Filter presented a fast convergence whereas Extended Kalman Filter had problems and only converged later on.

Documentos Relacionados

• Estimation of Pico-Satellite Attitude Dynamics and External Torques via Unscented Kalman Filter
• Analyzing the Unscented Kalman Filter Robustness for Orbit Determination Through Global Positioning System Signals
• Filtro não linear de Kalman sigma-ponto com algoritmo unscented aplicado a estimativa dinâmica da atitude de satélites artificiais
• Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter
• Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil