Unscented Kalman filter for spacecraft attitude estimation using Quaternions and Euler angles

Abstract

A spacecraft attitude estimation approach based on the Unscented Kalman Filter is derived. For nonlinear systems the Unscented Kalman Filter uses a carefully selected set of sample points to map more accurately the probability distribution than the linearization of the standard Extended Kalman Filter, leading to faster convergence from inaccurate initial conditions in attitude estimation problems. The filter formulation is based on standard attitude- vector measurements using a gyro-based model for attitude propagation. This paper compares the performance of a new technique, the Unscented Kalman Filter, when two different mathematical constructs are used to represent the attitude: the Euler angles and quaternions. In this study, the attitude of satellite is estimated with real time algorithms using real data supplied by gyros, Earth sensors and Sun sensors that are on board of the CBERS-2 (China Brazil Earth Resources Satellite).