Simultaneous Adaptation of the Process and Measurement Noise Covariances for the UKF Applied to Nanosatellite Attitude Estimation

Robust unscented Kalman filter with adaptation of process and measurement noise covariances

“…The main advantage of the UKF is that it does not use any linearization for calculating the state predictions and covariances. Many robust UKF variants have been proposed [30][31][32][33][34][35][36][37][38][39][40]. For instance, the H ∞ performance criterion has been combined with the UKF to improve the robustness against model errors and noise uncertainty in [30].…”

“…In [40], the fading factor is adopted to reduce the effect of the dynamics model errors and a robust estimation strategy is introduced to suppress the measurement model errors. Although the effect of the dynamic model error and the measurement error can be reduced simultaneously, the process and the measurement noise should be Gaussian distributions with known covariance matrices [37][38][39][40].…”

“…To this end, adaptation strategies have been proposed for process noise covariance in the UKF. For example, the robust UKF was developed for system fault detection in [37][38][39], where an adaptation scheme is carried out to detect the system fault. The application in nanosatellites attitude estimation shows that the robust UKF is fault tolerant against the sensor malfunctions.…”

Vision based Real-Time Navigation with Unknown and Uncooperative Space Target

“…At the predication step, the innovation is the difference between the actual measurement and its predicted value. On the other hand, the residual is the difference between actual measurement and its estimated value using the information available at step k. When both the R k and Q k matrices are estimated based on the innovation or residual covariance, Q k must be estimated assuming full knowledge of the R k and vice versa [238]. To run the Q k and R k at the same time when we have high uncertainties in both matrices, the Q k adaptation method presented here estimates the Q k matrix based on the innovation covariance, and the adaptation method for the R k matrix is a residual covariance-based scaling method.…”