Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Performance evaluation of UKF-based nonlinear filtering
Automatica (Journal of IFAC)
Bacterial foraging based moon symmetry axis estimation for spacecraft attitude determination
International Journal of Computer Applications in Technology
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Strapdown inertial navigation system (SINS) integrated with celestial navigation system (CNS) yields reliable mission capability and enhanced navigation accuracy for spacecrafts. A novel innovation-based adaptive estimation unscented Kalman filter (UKF) to solve the degradation performance caused by CNS unstable measurement disturbances in the SINS and CNS hybrid system is presented in this paper. The proposed adaptive unscented Kalman filter (AUKF) is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gains. After having deduced the proposed AUKF algorithm theoretically in detail, the approach is tested in the SINS/CNS integrated navigation system. Numerical simulation results show that the adaptive unscented Kalman filter outperforms the extended Kalman filtering (EKF) and conventional UKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution to the unknown changing measurement noise in the non-linear filter.