Multiplicative extended Kalman filter ignoring initial conditions for attitude estimation using vector observations

In this paper, the well-known multiplicative extended Kalman filter (MEKF) is re-investigated for attitude estimation using vector observations. From the Lie group theory, it is shown that the attitude estimation model is group-affine and its error state model should be trajectory-independent. Moreover, with such a trajectory-independent error state model, the linear Kalman filter is still effective for large initialisation errors. However, the measurement model of the traditional MEKF is dependent on the attitude prediction, which is therefore trajectory-dependent. This is also the main reason why the performance of traditional MEKF is degraded for large initialisation errors. Through substitution of the attitude prediction related term with vector observations in the body frame, a trajectory-independent measurement model is derived for MEKF. Meanwhile, MEKFs with reference attitude error definition and with global state formulating on special Euclidean group have also been studied, with the main focus on derivation of the trajectory-independent measurement models. Extensive Monte Carlo simulations of spacecraft attitude estimation implementations demonstrate that the performance of MEKFs can be much improved with trajectory-independent measurement models.

Automated summary

In this paper, the traditional multiplicative extended Kalman filter (MEKF) is reinvestigated for attitude estimation using vector observations. It is shown that the attitude estimation model is group-affine and its error state model should be trajectory-independent. By substituting attitude prediction related term with vector observations, a trajectory-independent measurement model is derived for MEKF. Extensive Monte Carlo simulations demonstrate that MEKFs with trajectory-independent measurement models can greatly improve performance.