MEKF Ignoring Initial Conditions for Attitude Estimation Using Vector Observations

TL;DR

This paper improves the multiplicative extended Kalman filter (MEKF) for attitude estimation by developing a trajectory-independent measurement model based on vector observations, enhancing performance especially with large initial errors.

Contribution

It derives a new trajectory-independent measurement model for MEKF using vector observations, addressing limitations of traditional MEKF in large error scenarios.

Findings

Enhanced MEKF performance with trajectory-independent measurement models

Significant improvement in attitude estimation accuracy

Validated through extensive simulations and field tests

Abstract

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 trajectory-independent error state model, the linear Kalman filter is still effective for large initialization 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 initialization errors. Through substitution of the attitude prediction related term with the vector observation in body frame, a trajectory-independent measurement model is derived for MEKF. Meanwhile, the MEKFs with reference attitude error definition and with global state formulating on special Euclidean group have also been studied, with main focus on derivation of the trajectory-independent measurement models. Extensive Monte Carlo simulations and field test of attitude estimation implementations demonstrate that the performance of MEKFs can be much improved with trajectory-independent measurement models.