Comparison of filtering techniques for relative attitude estimation of uncooperative space objects

Abstract

Nowadays, one of the most active research fields in space engineering is autonomous relative navigation around uncooperative objects. A common approach used to tackle this problem is through vision-based pose determination techniques. This paper investigates the possibility of using non-linear filtering techniques to improve the attitude estimation performance of vision-based methods. Furthermore, a simulation study is presented to compare the proposed nonlinear techniques with the multiplicative extended Kalman filter for attitude estimation. First-order and second-order nonlinear filters are adapted, implemented and tested for relative attitude estimation. Finally, the consequences of uncertainty in the knowledge of the target inertia matrix are investigated.