Influence of Process Noise Biases to Satellite Attitude Filters’ Estimates

In this study, an attitude filtering technique is proposed that adapts the bias type process noise uncertainties. To begin with, the Extended Kalman Filter (EKF) and Singular Value Decomposition (SVD) methods are combined to estimate a satellite’s attitude. Influence of the process noise bias type system changes to the innovation of EKF is investigated. It is proved that the bias type process noise change may be converted to the mean square of innovation of EKF and such type of changes can be compensated using the covariance scaling techniques. For the aim of estimating a satellite’s attitude, simulations are compared utilizing the adaptive and non-adaptive versions of the unconventional attitude filter in the presence of process noise bias. Three different forms of attitude estimation methods were investigated in the condition of process noise bias, and the findings results were compared. The simulation results show that, in the investigated cases the multiple fading factors based adaptive SVD-aided EKF can adapt to the changing environment better than the nonadaptive algorithms.