Iterated Unscented Kalman Filter for Spacecraft Attitude Estimation

In this paper, an iterated unscented quaternion estimator for spacecraft attitude estimation is proposed. A step length-varying iteration technique is incorporated into the unscented quaternion estimator and the corresponding termination condition is introduced to control the number of iterations and reduce the computational burden. The reference quaternion in the measurement update is redefined by the developed iteration technique. Simulation results demonstrate the superiority of the proposed new algorithm in comparison with the multiplicative extended Kalman filter, norm constraint Kalman filter and the unscented quaternion estimator under large initialization condition errors. It also shows that the proposed algorithm exhibits a faster convergence rate.